Two-Stage Federated Compression Using VQ-VAE and Arithmetic Coding
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Solution Overview
Problem
Current data compression methods struggle to efficiently compress large datasets without losing information, particularly in low-latency applications like satellite telemetry systems.
Innovation Solution
A system and method for federated two-stage compression with federated joint learning, utilizing a Variational Autoencoder with Vector Quantization on an edge server, followed by probability prediction driven arithmetic coding, and a large codeword model core for efficient lossless data compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compression algorithms are used, then compression speed is improved, but compression ratio deteriorates
Solution Approach 1:
The patent divides the compression process into two distinct stages: a first compression stage using traditional algorithms for speed, and a second compression stage using learned models for higher compression ratios. This segmentation allows each stage to optimize for its specific function, resolving the contradiction between speed and compression ratio.
Solution Approach 2:
The patent introduces an intermediary representation (latent space encoding) between the original data and the final compressed form. This intermediary stage allows the data to be transformed into a format that can be efficiently compressed by both traditional and learned methods, improving overall compression ratio without sacrificing speed.
2Loss of information
If lossy compression is used, then compression ratio is improved, but information loss increases
Solution Approach 1:
The patent employs dynamic adaptive coding where the compression strategy adjusts based on the importance of different data components. Less important components undergo lossy compression while critical information is preserved through lossless methods, achieving high compression ratios without significant information loss.
Solution Approach 2:
The patent changes the compression parameters adaptively based on data characteristics and requirements. By adjusting compression strength, quantization levels, and coding parameters dynamically, the system achieves high compression ratios while maintaining reliability for critical information.
3Productivity
If complex learned compression models are deployed, then compression efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent segments the compression system into a complex learned model component and a simpler traditional compression component. The learned model is trained offline to capture complex patterns, while the actual compression process uses lighter-weight operations, reducing real-time computational complexity while maintaining high efficiency.
Solution Approach 2:
The patent performs preliminary training of complex compression models offline before deployment. This preliminary action transfers complex computational requirements to the training phase, allowing the deployed system to use more efficient inference operations with reduced real-time computational complexity.
4Loss of information
If high compression ratios are achieved, then data size is reduced, but decompression time increases
Solution Approach 1:
The patent segments the compression and decompression operations into distinct stages that mirror each other. The first compression stage has a corresponding fast decompression stage, and the second stage uses pre-computed lookup tables and efficient decoding algorithms to minimize decompression time while achieving high compression ratios.
Solution Approach 2:
The patent performs preliminary computation of compression tables, codebooks, and transformation parameters during the compression phase. This preliminary action prepares data in a form that enables rapid decompression, reducing decompression time while maintaining high compression ratios through efficient representation.
Data Source
AI summary
A system and method for federated two-stage compression with federated joint learning. The system and method proposed allow for fast and efficient lossless data compression of a large variety of data types. The system and method have a variety of real-world applications, including deep learning solutions for telemetry, tracking, and command subsystems for satellites. Satellites and their control centers are incredibly spaced apart which makes data compression an extremely important process to transmit large sets of information in a low-latency, high-efficiency environment. The proposed system and method utilize probability prediction driven arithmetic coding which provides faster encoding times and higher compression ratios when paired with a long short-term memory system for data compression.