Decentralized Federated Learning With Neighbor Parameter Aggregation
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Solution Overview
Problem
Conventional machine learning systems face challenges such as high communication costs, privacy concerns, and robustness issues due to centralized data processing, which can lead to abrupt failures when centralized servers fail.
Innovation Solution
Implementing decentralized federated learning systems where each client trains a local model using its private dataset, sharing only model parameters and weighting coefficients with neighbors, reducing reliance on a central server and enhancing system robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized data processing is used, then machine learning models can be trained using all available data, but communication costs increase significantly and data privacy is compromised
Solution Approach 1:
The patent segments the centralized training process into distributed local training at multiple client devices. Each client trains a local model on its own data, and only model parameters (not raw data) are aggregated. This segmentation eliminates the need to transfer large volumes of raw data to a central server, significantly reducing communication costs while maintaining model training effectiveness through parameter aggregation.
Solution Approach 2:
The patent extracts only the essential model parameters from the training process and transfers them for aggregation, leaving the bulk of data localized at each client. This extraction approach allows the system to benefit from collective learning without the communication overhead of transferring complete datasets, resolving the contradiction between training accuracy and communication cost.
2Reliability
If centralized data processing is used, then machine learning models can be trained using all available data, but data privacy is compromised
Solution Approach 1:
The patent segments data processing across multiple decentralized client devices, ensuring that raw data never leaves its local device. Each client contributes only aggregated model parameters to the collective training process, maintaining data privacy while still enabling the system to learn from diverse data sources across all clients.
Solution Approach 2:
The patent introduces model parameters as an intermediary between local data and the collective model. Instead of directly sharing or centralizing sensitive raw data, the system uses parameter aggregation as an intermediary mechanism that preserves privacy while achieving the goal of collective learning. This intermediary approach allows accurate model training without exposing private data.
3Ease of operation
If centralized server architecture is used, then machine learning training can be coordinated efficiently, but system robustness decreases due to single point of failure
Solution Approach 1:
The patent segments the centralized coordination function into distributed peer-to-peer interactions among clients. Each client independently communicates with neighbors to exchange model parameters, eliminating the single point of failure represented by the central server. This segmentation maintains training coordination through decentralized consensus while significantly improving system robustness.
Solution Approach 2:
The patent inverts the traditional centralized coordination model by allowing clients to autonomously coordinate with each other without central control. Instead of the server directing training operations, clients self-organize through peer-to-peer parameter exchange, achieving efficient coordination through distributed consensus while eliminating the vulnerability of centralized architecture.
4Reliability
If decentralized federated learning is implemented, then data privacy is maintained and system robustness improves, but communication latency increases due to multiple connection hops
Solution Approach 1:
The patent applies local quality by having each client train and optimize its own local model using its own data, rather than requiring centralized processing. This local optimization reduces the need for repeated full-data transmissions, decreasing communication latency while maintaining privacy. Each client performs multiple local training iterations before parameter aggregation, making the decentralized approach more efficient.
5Reliability
If decentralized federated learning is implemented, then system robustness improves by distributing workload, but device complexity increases due to direct peer-to-peer communication requirements
Solution Approach 1:
The patent applies universality by designing a standardized parameter exchange protocol that all clients follow uniformly, regardless of their specific hardware or data characteristics. This universal interface simplifies the peer-to-peer communication architecture, reducing device complexity while maintaining the robustness benefits of decentralization. Each client implements the same basic functionality for parameter reception, processing, and transmission.
Data Source
AI summary
Methods and systems for decentralized federated learning are described. Each client participating the training of a local machine learning model identifies one or more neighbor clients in direct communication with itself. Each client transmits to its neighbor clients a weighting coefficient and a set of local model parameters for the local model. Each client also receives from its neighbor clients respective sets of local model parameters and respective weighting coefficients. Each client updates its own set of local model parameters using a weighted aggregation of the received sets of local model parameters, each received set of local model parameters being weighted with the respective received weighting coefficient. Each client trains its local machine learning model using a machine learning algorithm and its own local dataset.