System for AI-assisted data compression in distributed systems with high throughput

Abstract

A system for AI-assisted data compression in distributed high-throughput systems, consisting of: an AI-driven encoder-decoder module configured to analyze incoming data streams and apply dynamic compression using machine learning models, where the encoder uses a transformer-based neural network to identify recurring patterns in data and generate compressed representations, and the decoder reconstructs the original data with high fidelity by adaptively refining the decompression parameters; a federated neural coding processing unit configured to operate across a plurality of distributed nodes, where each node maintains a local AI-based compression agent trained on regional data patterns, and the agents regularly synchronize the compression models through a decentralized training process without exchanging raw data, thereby optimizing compression efficiency while maintaining data privacy; a plurality of AI processing agent units, each configured to optimize compression strategies by continuously evaluating trade-offs between compression ratio, computational complexity, and network congestion, with the reinforcement learning agents receiving real-time feedback from system performance metrics and dynamically adjusting the encoding parameters accordingly; A self-regulating feedback loop integrated into the AI-driven encoder-decoder module. The feedback loop includes a monitoring subsystem that collects real-time system metrics such as bandwidth availability, CPU utilization, and data fidelity. This feedback is continuously fed into the compression model to refine the encoding parameters and prevent performance degradation. A blockchain-based audit layer consisting of a decentralized ledger that records compression operations performed across distributed nodes. The blockchain layer is configured to cryptographically sign each compression event, ensuring data integrity, traceability, and tamper resistance for compliance-sensitive applications. A hardware-accelerated AI compression processor with a dedicated neural network inference chip configured to offload compression tasks from general-purpose processors. The processor integrates parallelized tensor processing cores to accelerate neural model execution and minimize computational overhead in real-time environments.

Description

Field of the invention

[0001] The present invention relates to a system and method for AI-assisted data compression in distributed high-throughput systems. In particular, the invention relates to a unique architecture that seamlessly combines machine learning models for adaptive and lossless data compression in distributed networks of interconnected nodes in large-scale computing systems such as cloud computing, edge computing, and other data-intensive applications with real-time analytics and storage optimization requirements. Background of the invention

[0002] The rapid growth of digital data from a variety of sources, including IoT (Internet of Things) devices, high-resolution imaging systems, financial transactions, and multimedia applications, makes data compression mechanisms increasingly important. Conventional data compression techniques such as Huffman coding, Lempel-Ziv-Welch (LZW), and wavelet transforms cannot dynamically adapt to corrupted data and computational resources in distributed systems. Furthermore, the latest compression techniques fail to achieve an optimal balance between compression ratio, speed, and energy efficiency while minimizing data loss in high-throughput environments.

[0003] AI / ML methods have the potential to improve compression efficiency by detecting redundancies in the encoding data, predicting optimal compression strategies, and dynamically adapting encodings based on system status. However, modern AI-based compression methods do not necessarily work smoothly in distributed architectures characterized by dynamic fluctuations in bandwidth, latency, and compute resources.

[0004] Therefore, there is a need for an AI-powered data compression system that not only enables better compression but can also adapt to the different constraints of the systems without losing data integrity.

[0005] While lossless compression algorithms such as Huffman or arithmetic coding are efficient for structured data and text, they tend to be inefficient for complex, high-entropy domains such as images, audio, or unstructured protocols. Huffman coding, on the other hand, uses a frequency-based approach to assign shorter binary codes to frequently occurring symbols. However, this approach works excellently for predictable data and fails miserably for dynamically changing information streams. Likewise, arithmetic coding achieves better compression efficiency than traditional block or Huffman coding schemes, which express complete messages as single floating-point numbers. However, the computational overhead and complexity of forming these sequences limit its applicability in high-throughput, real-time applications.However, the commonly used Lempel-Ziv-Welch (LZW) compression algorithm, which creates a dictionary of common data patterns to minimize redundancy, faces major scalability issues, especially in scenarios with rapid updates and unpredictable data distributions, resulting in poor optimization of the static dictionary.

[0006] With lossy compression, common techniques such as JPEG, MPEG, MP3, and wavelet-based compression significantly reduce the required file size by discarding seemingly unimportant data. While these techniques are well-suited for applications that require media, they cause irreparable damage to the information and are therefore unsuitable for areas such as scientific computing, where not only the data must be precise, but every bit is important. Furthermore, they typically ignore system boundaries and do not adapt in real time to network congestion, computational load, or application specifics.

[0007] Developments in distributed computing environments, including cloud storage networks, content delivery systems, and edge computing architectures, pose new challenges for compression. The goal of compression is not only to compress data but also to intelligently adapt to different infrastructures with heterogeneous properties. Most current solutions use either a static or semi-adaptive compression approach, which cannot adapt performance to the varying aspects of workloads and the network. In fact, common cloud-based storage systems such as grip and bzip2 use static, dictionary-based compression, which requires significant preprocessing and consumes significant computational power when processing distributed datasets.These limitations impact environments that require timely data transmission, including video streaming platforms, autonomous vehicle networks, and industrial IoT devices.

[0008] The other category of existing compression solutions are predictive coding techniques such as delta coding and Burrows-Wheeler transform (BWT)-based methods, which focus on learning data models to enable more retroactive coding. Predictive coding encodes only changes in consecutive data, helping to reduce duplicate records in time series or structured logs, as shown below. These methods can be effective for some datasets, but are often inadequate for irregular, high-entropy datasets where patterns are difficult to detect, resulting in suboptimal compression ratios and higher computational overhead.

[0009] Recent advances in AI and machine learning have prompted researchers to explore deep learning-based compression techniques, particularly for image and video coding. Neural networks such as convolutional auto encoders (CAEs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) show some superior performance in learning data distributions and generating small representations of the input data with very low information loss. Deep generative models, particularly variational auto encoders (VAEs), have demonstrated impressive performance in applications such as image super-resolution, speech synthesis, and anomaly detection. However, their integration into distributed computing environments is challenging because neural network inference requires large computational resources, and significant generalization of such models may result in only a few data types.

[0010] [AI-assisted compression] Many commercial AI-assisted compression solutions attempt to optimize traditional compression methods through machine learning. For example, Google's Deep Mind has tested AI-assisted video compression, which can adapt encoding parameters in real time to the complexity of the scene. This is similar to Facebook AI Research's work with neural network entropy models for lossless compression. However, these techniques were developed for centralized computing environments that use powerful GPUs or TPUs to execute the model. Because the large-scale deployment of AI models is prohibitively expensive, they have not yet found widespread adoption in distributed high-throughput systems (e.g., edge computing nodes).

[0011] Furthermore, AI-based compression methods tend to strike a trade-off between compression efficiency and computational complexity. Neural network-based compressors require large amounts of data for training and can exhibit high inference latency, which is typically the bottleneck in real-time applications. This becomes even more challenging because most AI models are trained on datasets that don't always translate well to the everyday needs of a distributed system. This rigidity leads to poor performance on time-varying data distributions, such as dynamic network packets, sensor data streams, and multi-source cloud workloads.

[0012] Currently available alternatives may be limited by another important aspect in which we specialize: decentralization, since most available alternatives depend on their provider's computational resources. Most compression methods have traditionally been based on a centralized processing model, where data is collected, compressed, and sent by a single processing unit. Distributed environments, however, require compression techniques that operate cooperatively across multiple nodes and carefully optimize the trade-offs between intra-node and inter-node computations (communication costs). Recent work explores federated learning approaches to compression, where AI models are trained across distributed nodes without transmitting raw data to a central server.We expect multi- and single-cell applications soon, ranging from immune repertoire profiling (single-cell sequencing) to drug dosage prediction. However, these approaches are still in development and are not necessarily optimally suited for high-throughput and real-time applications.

[0013] Existing solutions typically don't offer adaptive, real-time feedback control that makes decisions based on currently available performance metrics in the system. Most conventional methods rely on specific heuristics or immutable models that don't account for differences in network bandwidth, CPU utilization, or memory constraints. While AI-based methods are flexible, they require additional training under changing conditions, even when raids are random. This is extremely inefficient in production environments with extremely dynamic workloads.

[0014] Existing compression systems also raise concerns about security and data integrity. Conventional algorithms may lack implemented protection mechanisms to ensure the integrity of the output compressed data, which could be corrupted, lost, or tampered with during transmission. Although blockchain-based audit trails have been proposed as a potential solution, their integration into compression systems remains the subject of ongoing research. Despite their potential applications, AI-driven methods can learn a comprehensive data representation but potentially have biases or vulnerabilities that could lead to data contamination in mission-critical applications.

[0015] The present invention aims to overcome these limitations through a new configurable extension framework of data compression that leverages both traditional compression methods and intelligent machine learning models, multi-agent reinforcement learning strategies, and federated neural coding processes to achieve superior performance in demanding, distributed, and scalable multi-agent contexts. Summary of the invention

[0016] This patent describes a method for AI-assisted data compression. It uses an adaptive compression framework based on machine learning to optimize data transmission and storage in distributed, high-throughput systems. The system uses a federated neural coding unit that effectively aggregates the neural representations of multiple nodes to iteratively learn the patterns in the data, thus achieving increasingly precise and efficient compression.

[0017] The core of the system is an AI-based encoder-decoder module based on a hybrid approach that combines neural networks with conventional compression algorithms. The encoder uses deep recurrent networks and transformer-based architectures to detect recurring patterns and encode them into highly compact representations with minimal information loss.

[0018] Furthermore, this invention includes an edge-smart compression layer running at the edge of the network to reduce data traffic by shrinking the data and sending it only to central compute nodes. Edge Compression Unit: We develop a lightweight convolutional auto-encoder trained on patterns from historical data based on user behavior to achieve real-time compression with minimal computational overhead.

[0019] Furthermore, the invention includes a self-optimizing feedback loop that relearns the compression model from scratch with each new video reception. It relies on real-time metrics such as bandwidth availability, processing load, and storage efficiency, and uses this data to iteratively improve compression with each pass. The introduction of a blockchain-based audit layer ensures data integrity and robustness for the traceability of the compression process across the distributed nodes in the system.

[0020] The overall goal of the invention is to develop an AI-assisted data reduction method to increase the performance, flexibility, and scalability of volume-passaging compression for distributed databases. This invention overcomes the limitations of traditional and modern compression methods by leveraging a comprehensive compression approach that uses artificial intelligence and machine learning for adaptive feedback, optimization, and adjustment of overall performance to system conditions. The method learns data and, once the software is installed, intelligently applies the optimal compression for different data types. Our goal is to enable on-the-fly compression and decompression in large-scale distributed systems such as cloud storage, edge computing, content delivery networks, and IoT dataflow.

[0021] The novel federated neural coding mechanism can learn optimal compression patterns jointly from multiple federated distributed nodes without requiring data to be aggregated at a central location. The present invention is based on a decentralized machine learning model trained to intelligently compress data at the data source using edge cloud distributed computing and refine its compression strategy as data is received via different nodes in the network. This allows the strategy to be dynamically adjusted and the overall transmission load to be reduced by sending less data to the high-throughput end. This minimizes the overall latency through distributed computing across edge nodes using the machine learning model.Furthermore, the federated nature of this process improves both data privacy and security, as raw data doesn't need to be sent to a central location for compression. This reduces the risk of data leaks and unauthorized access.

[0022] Furthermore, the invention aims to integrate a multi-agent reinforcement learning framework that can continuously adjust compression parameters based on real-time observations of the distributed system. To adapt to changing environmental requirements, multiple AI agents are available, trained to optimize the trade-off between compression efficiency, computational load, and network congestion. This allows the compression system to evolve and adapt over time, keeping pace with changes in data distribution, network conditions, and system load without requiring manual intervention or frequent retraining.

[0023] This invention also aims to implement an edge-intelligent compression layer to improve real-time processing. Such a layer can perform preliminary compression at the network edge before forwarding data to the main processing centers. This improves bandwidth utilization and shortens the response times of latency-sensitive applications. Data from edge devices is compressed before being transmitted via low-latency, lightweight convolutional auto-encoders in the edge compression unit. This reduces the load on the computing power of the edge devices.

[0024] Another goal of this invention is to ensure the integrity, traceability, and security of compressed data. The system utilizes a blockchain audit layer to introduce a decentralized and tamper-proof logging mechanism that records all compression and decompression operations. This innovation ensures data integrity and enables the auditing of compression operations. At the same time, the secure and transparent recording of compression operations prevents unauthorized changes. This is becoming increasingly important in regulated industries such as finance, healthcare, and the public sector, where data authenticity and auditability play a key role.

[0025] The invention also aims to create a highly efficient yet resource-efficient compression framework for use in a wide range of computing environments, from high-throughput data centers to resource-constrained edge devices. By leveraging advanced AI accelerator hardware, vectorized tensor cores, and efficient neural network topologies, the system achieves compression efficiency that loops would never achieve without the host's computational load. It enables the benefits of AI-driven compression to be deployed in environments with limited processing capabilities.

[0026] Another key goal is seamless interoperability with existing compression standards and frameworks. The invention is compatible with common data formats, storage systems, and communication protocols, enabling companies to integrate the AI-powered compression system without extensive architectural changes. A cloud-enabled platform is easy to deploy through an API-based framework, enabling broad adoption across various industries—from cloud service providers to distributed databases to enterprise storage. SHORT DESCRIPTION OF THE FIGURE

[0027] These and other features, aspects, and advantages of the present invention will become more readily understood when the following detailed description is read in conjunction with the accompanying drawings, in which like characters represent like parts throughout. Fig. Figure 1 shows a block diagram of a system for AI-assisted data compression in distributed high-throughput systems.

[0028] Those skilled in the art will also appreciate that the elements in the drawings are shown for convenience and are not necessarily to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to device construction, one or more components of the device may be represented in the drawings by conventional symbols. The drawing may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawing with details that would be readily apparent to those skilled in the art from the present description. Detailed description of the invention

[0029] To facilitate understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and a clear description will be given. However, the scope of the invention is not limited thereby. Changes and further modifications to the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to one skilled in the art to which the invention pertains.

[0030] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0031] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, the language "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not refer to the same embodiment.

[0032] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps may include not only those steps, but also additional steps not expressly listed or inherent in that process or method. Likewise, the statement "comprises" for one or more devices, subsystems, elements, structures, or components does not exclude, without further limitation, the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. The systems, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0034] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0035] In Fig.A block diagram of a quantum-assisted system for detecting fake news is shown. The system 100 includes: an AI-driven encoder-decoder module (102) that analyzes incoming data streams and applies dynamic compression using machine learning models. The encoder uses a transformer-based neural network to detect recurring patterns in the data and generate compressed representations. The decoder reconstructs the original data with high accuracy by adaptively refining the decompression parameters. A federated neural coding unit (104) operates across multiple distributed nodes. Each node manages a local AI-based compression agent trained on regional data patterns. The agents regularly synchronize the compression models via a decentralized training process without sharing raw data.This optimizes compression efficiency while preserving data privacy. Multiple AI processing agent units (106), each configured to optimize compression strategies by continuously evaluating trade-offs between compression ratio, computational complexity, and network congestion. The reinforcement learning agents receive real-time feedback from system performance metrics and dynamically adjust the encoding parameters accordingly. An edge-intelligent compression layer (108) deployed at the network edge nodes.The edge compression layer includes a lightweight convolutional auto-encoder pre-trained on historical data patterns and configured to perform preliminary data compression at edge locations before transmission to central compute nodes, thereby reducing bandwidth consumption and improving real-time processing efficiency; a self-regulating feedback loop (110) integrated with the AI-driven encoder-decoder module. The feedback loop includes a monitoring subsystem that collects real-time system metrics such as bandwidth availability, CPU utilization, and data fidelity.The feedback is continuously fed into the compression model to refine the coding parameters and prevent performance degradation; a blockchain-based audit layer (112) comprising a decentralized ledger that records compression operations performed across distributed nodes, wherein the blockchain layer is configured to cryptographically sign each compression event, thus ensuring data integrity, traceability, and tamper-evident security for compliance-sensitive applications; a hardware-accelerated AI compression processor (114) comprising a dedicated neural network inference chip configured to offload compression tasks from general-purpose processors, wherein the processor integrates parallelized tensor processing cores to accelerate the execution of neural models and minimize computational overhead in real-time environments.

[0036] In one embodiment, the AI-driven encoder-decoder module (102) further comprises a hierarchical attention-based compression mechanism that dynamically adjusts feature extraction weights based on data entropy, wherein the hierarchical attention mechanism optimizes the compression of complex high-dimensional datasets while preserving important information structures.

[0037] In one embodiment, the federated neural coding processing unit (104) uses a federated averaging algorithm to synchronize locally trained compression models across nodes. The synchronization process incorporates differential privacy techniques to ensure that no raw data is exposed during model updates, thereby improving data security in distributed environments.

[0038] In one embodiment, the multi-agent reinforcement learning framework (106) uses an asynchronous actor-critic (A3C) reinforcement learning model. The model trains a plurality of independent agents to explore and exploit optimal compression strategies, with each agent receiving separate reward signals based on compression performance metrics, including latency reduction, energy efficiency, and data reconstruction accuracy.

[0039] In one embodiment, the edge-intelligent compression layer (108) further comprises a real-time adaptive quantization module that dynamically adjusts bit depth reduction levels based on data variability, wherein the adaptive quantization module minimizes information loss in compressed data while reducing computational overhead on resource-constrained edge devices.

[0040] In one embodiment, the self-regulating feedback loop (110) further comprises a reinforcement learning-based self-adaptive module that autonomously adjusts the hyperparameters of the neural network in response to changing system conditions, wherein the self-adaptive module prevents performance degradation during peak load periods by changing the complexity of the compression model in real time.

[0041] In one embodiment, the blockchain-based audit layer (112) further comprises a zero-knowledge proof (ZKP) based verification module that enables independent validation of compression events without disclosing confidential data, wherein the ZKP verification ensures compliance with data confidentiality standards while enabling auditability of compression operations.

[0042] In one embodiment, the hardware-accelerated AI compression processor (114) comprises an integrated neuromorphic coprocessor executing Spiking Neural Network (SNN) models for ultra-low-power compression tasks, where the neuromorphic architecture enables real-time adaptation to dynamic workload conditions in power-constrained environments such as IoT devices.

[0043] In one embodiment, the AI-driven encoder-decoder module (102) is further configured to implement a hybrid lossless compression framework in which low-entropy data regions are losslessly compressed using entropy coding and high-entropy regions are selectively compressed using learned lossy transformations. The hybrid framework maximizes compression efficiency while maintaining data reconstruction quality.

[0044] In one embodiment, the federated neural coding processing unit (104) further comprises a reinforcement learning-based federated scheduler that prioritizes model synchronization between nodes based on available computational resources, wherein the federated scheduler reduces synchronization delays and optimizes model convergence in large-scale distributed environments.

[0045] The artificial intelligence (AI)-enhanced data compression system facilitates compression and decompression, enabling data storage and transmission in high-throughput distribution channel environments. This process incorporates deep learning models and various models for different data types, particularly for creating encodings with true data fidelity. This process forms the heart of the system, the AI-based encoder-decoder module. The encoder part uses a transformer-based neural network design that leverages self-attention techniques to detect recurring patterns in diverse, high-dimensional datasets. Conventional compression methods use fixed encoding schemes, whereas our transformer-based model streamlines its feature extraction process based on data entropy, thus optimizing compression effectiveness for heterogeneous data.In contrast, during reconstruction, a reinforcement learning (RL)-based optimization layer refines the decompression parameters in each round by minimizing reconstruction errors and keeping the sources in the configuration with the least information loss.

[0046] The invention further integrates a federated neural coding technique that can both compress information clusters and train such compression models across many compute nodes without the need to centralize raw data. This enables scalability and adaptability in the distributed system. It utilizes a federated averaging process, in which each node develops localized AI-based compression agents depending on the data distribution. Furthermore, the decentralized accumulation of periodic model updates is enabled, allowing the model to continuously learn and improve compression rates without disclosing sensitive information. This is achieved by integrating differential privacy mechanisms into the federated system, ensuring that no identifiable information is transmitted during model exchange.The federated approach not only reduces the cost of centralized compression but also enables its application.

[0047] One of the system's key innovations is the Multi-Agent Reinforcement Learning (MARL) framework, which trains multiple AI agents to dynamically optimize compression strategies. Each agent must weigh the trade-offs between compression ratio, computational effort, and network latency. Based on the Asynchronous Actor-Critic (A3C) reinforcement learning model, these agents enable independent exploration of compression strategies, while a global value function facilitates policy updates. The agents receive real-time performance metrics such as bandwidth availability, system congestion, and compression latency and adjust the encoding parameters accordingly. This architecture automatically optimizes itself based on different operating conditions, ensuring that bottlenecks will not occur even under extreme data loads in the future.

[0048] The invention further comprises an edge-intelligent compression layer that efficiently reduces data volume by operating network edge nodes, thus improving the responsiveness of the entire system. This layer employs a lightweight convolutional auto-encoder (CAE) to achieve unsupervised feature extraction from raw data and compress recurring patterns before sending them to the central compute nodes. An adaptive quantization module minimizes information loss by dynamically controlling the bit depth reduction stages based on data variability measurements to minimize bandwidth consumption. An edge-oriented approach reduces latency and enables real-time computing for use cases such as video analytics, autonomous vehicle networks, and the factories of the future.

[0049] One of the biggest challenges of AI-based compression is avoiding performance degradation in different system environments. To this end, the invention uses a self-regulating feedback mechanism that iteratively adapts the compression model based on current measured values. The feedback loop includes a self-adaptive module based on reinforcement learning. This enables the neural compression model to autonomously adjust its hyperparameters to the different workload conditions. Thus, in addition to the compression model, the system dynamically adapts and acts in such a way that efficiency is maintained even under this workload and the model efficiently saves memory. In addition, the system integrates an adversarial learning-based anomaly detection module that detects the trade-offs resulting from the introduction of coding artifacts due to suboptimal coding.By using unconstrained optimization formulations in a generative adversarial network (GAN) (separation structure), this module improves compression results to avoid data degradation in mission-critical applications.

[0050] To ensure the integrity and security of the compressed data, a blockchain-based audit layer is implemented that logs the compression operations in a decentralized, tamper-proof ledger. Each compression event can be cryptographically signed and stored in a shared blockchain storage architecture. This enables parallel retrieval of audit logs while minimizing transaction latency. Watchtower is an independent verification component for compression events generated by the Tender Mint layer. At the same time, Watchtower provides a blockchain-level ZKP-based audit module to validate the integrity of compression events without exposing sensitive information.In industries such as finance, healthcare, and public sector, where data authenticity and auditability are of utmost importance, a permanent, tamper-proof, blockchain-based audit trail ensures regulatory compliance.

[0051] At the heart of the invention is a hardware-accelerated AI compression processor that frees standard processors from demanding, intensive compression operations. The processor integrates a neuromorphic coprocessor for executing spiking neural network (SNN) models. This enables compression operations to be performed with extremely low power consumption in energy-constrained environments such as IoT devices. The processor also features a dynamically reconfigurable neural processing unit (NPU) that enables thematic redistribution of tensor processing loads based on real-time system threshold loads. By leveraging these hardware-optimized acceleration methods, the invention achieves high-performance AI-based compression while ensuring energy efficiency.

[0052] The AI-powered encoder-decoder block uses a hybrid lossless compression strategy to optimize compression efficiency for different data types. It uses entropy-based lossless compression for low-entropy data regions and learned lossy transformations selectively for high-entropy regions. This sophisticated system achieves better compression ratios while maintaining data integrity by dynamically switching compression modes based on data characteristics. Some are user-defined, while the rest are based on an evolutionary algorithm—an optimization engine that learns to discover even more optimal neural network architectures through historical workload analysis (“learning by experience”). This means that the compression model constantly evolves with the processing flood.

[0053] For streaming applications with continuous data, the edge-intelligent compression layer features a differential compression module that calculates and transmits exclusively the differences between consecutive data frames. This process significantly minimizes the transmission of redundant information in areas such as telemetry, remote sensing, and real-time video processing. The blockchain-based audit layer also includes a cryptographic timestamping mechanism with an immutable record, enabling verification of the temporal integrity of compressed data sets.

[0054] It is suitable for large, distributed environments with AI-assisted deep data compression. The system leverages transformer-based neural networks, federated learning, reinforcement learning, blockchain auditing, and hardware acceleration to enable more flexible and robust compression. It pushes the boundaries of traditional compression while offering high efficiency, scalability, and security. This method of aggregate data compression outperforms existing techniques through real-time adaptability, decentralized training for maximum efficiency, edge computing optimization, and cryptographic verification of the integrity of model contributions.

[0055] The effect of the invention can be realized in a computer system comprising, for example, the following components: For the software implementation, we create a deep learning-based compression library with Tensorflow-tensorRT and distributed execution; for the hardware implementation, we use a dedicated AI accelerator chip that can take over compression tasks from general-purpose processors using the same library. The AI ​​accelerator, designed as a tensor processing unit, uses parallelized processor cores to accelerate the inference of various neural networks, thus reducing the processing overhead of host systems.

[0056] It should be noted that the system and method for model-based, AI-assisted data compression described here exclusively benefits open-form and ADMM-based neural networks by increasing their compressibility and scalability, as enormous real-world data distributions become readily available. The technology leverages advanced concepts such as deep learning, federated neural coding, reinforcement learning, and blockchain-based auditing, enabling a scalable and adaptive real-time data compression approach for the current computing landscape. The dynamic learning and optimization capabilities in the compression process promise a revolution in cloud storage, edge computing, and data-intensive applications.

[0057] The drawings and the foregoing description illustrate examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined to form a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the order shown; nor do all actions need to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and use of materials, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0058] Advantages, further advantages, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, advantages, solutions to problems, and any components that may result in or enhance an advantage, benefit, or solution are not to be construed as critical, required, or essential features or components of any or all of the claims. REFERENCES 100 A quantum-based system for detecting fake news is presented. 102 AI-controlled encoder-decoder module 104 Federated Neural Coding Processing Unit 106 AI processing agent units 108 Edge-intelligent compression layer 110 Self-regulating feedback loop 112 Blockchain-based audit layer 114 Compression processor

Claims

[1] A system for AI-assisted data compression in distributed high-throughput systems, consisting of: an AI-driven encoder-decoder module configured to analyze incoming data streams and apply dynamic compression using machine learning models, where the encoder uses a transformer-based neural network to identify recurring patterns in data and generate compressed representations, and the decoder reconstructs the original data with high fidelity by adaptively refining the decompression parameters; a federated neural coding processing unit configured to operate across a plurality of distributed nodes, where each node maintains a local AI-based compression agent trained on regional data patterns, and the agents regularly synchronize the compression models through a decentralized training process without exchanging raw data, thereby optimizing compression efficiency while maintaining data privacy; a plurality of AI processing agent units, each configured to optimize compression strategies by continuously evaluating trade-offs between compression ratio, computational complexity, and network congestion, with the reinforcement learning agents receiving real-time feedback from system performance metrics and dynamically adjusting the encoding parameters accordingly; A self-regulating feedback loop integrated into the AI-driven encoder-decoder module. The feedback loop includes a monitoring subsystem that collects real-time system metrics such as bandwidth availability, CPU utilization, and data fidelity. This feedback is continuously fed into the compression model to refine the encoding parameters and prevent performance degradation. A blockchain-based audit layer consisting of a decentralized ledger that records compression operations performed across distributed nodes. The blockchain layer is configured to cryptographically sign each compression event, ensuring data integrity, traceability, and tamper resistance for compliance-sensitive applications. A hardware-accelerated AI compression processor with a dedicated neural network inference chip configured to offload compression tasks from general-purpose processors. The processor integrates parallelized tensor processing cores to accelerate neural model execution and minimize computational overhead in real-time environments. [2] The system of claim 1, wherein the AI-driven encoder-decoder module further comprises a hierarchical attention-based compression mechanism that dynamically adjusts feature extraction weights based on data entropy, wherein the hierarchical attention mechanism optimizes the compression of complex high-dimensional datasets while preserving important information structures. [3] The system of claim 1, wherein the federated neural coding unit uses a federated averaging algorithm to synchronize locally trained compression models across nodes, wherein the synchronization process includes differential privacy techniques to ensure that no raw data is exposed during model updates, thereby improving data security in distributed environments. [4] The system of claim 1, wherein the self-regulating feedback loop further comprises a reinforcement learning-based self-adaptive module that autonomously adjusts the hyperparameters of the neural network in response to changing system conditions, the self-adaptive module preventing performance degradation during high-load periods by changing the complexity of the compression model in real time. [5] The system of claim 1, wherein the hardware-accelerated AI compression processor comprises an integrated neuromorphic coprocessor executing Spiking Neural Network (SNN) models for ultra-low-power compression tasks, the neuromorphic architecture enabling real-time adaptation to dynamic workload conditions in power-constrained environments such as IoT devices.

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