Federated Learning Weight Compression with Quantization and Huffman Coding
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Solution Overview
Problem
Existing federated learning methods face inefficiencies in communication compression, particularly in bandwidth occupation, as they rely on sparse gradient compression methods that do not fully optimize the transmission of model weight information, leading to suboptimal convergence rates and increased communication costs.
Innovation Solution
A communication compression method based on model weight distribution, utilizing scalar quantization and Huffman coding, where the distribution of model weights is quantized using the Lloyd-Max algorithm and then encoded with Huffman coding to minimize mean square quantization error and reduce communication bits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sparse gradient compression methods are used to reduce communication bandwidth, then bandwidth occupation is reduced, but transmission accuracy and convergence rate are suboptimally affected
Solution Approach 1:
The patent changes the compression approach from gradient-based sparse compression to model weight-based scalar quantization. By analyzing the probability distribution of model weights and applying scalar quantization with Huffman coding, the system achieves better compression efficiency while maintaining transmission accuracy, resolving the contradiction between bandwidth reduction and accuracy preservation
Solution Approach 2:
The patent replaces the traditional gradient compression mechanism with a model weight compression mechanism. Instead of compressing gradient updates using sparse methods, the system compresses model weights through scalar quantization based on their probability distribution, substituting one compression paradigm with a more effective approach that better balances bandwidth and accuracy
2Quantity of substance
If gradient compression is applied to reduce traffic cost, then communication cost is reduced, but the compression efficiency is insufficient as two communications are needed for one model update
Solution Approach 1:
The patent changes from gradient compression to model weight compression, utilizing the probability distribution characteristics of model weights. By applying scalar quantization and Huffman coding to model weights, the system achieves higher compression efficiency that can complete model updates with fewer communication rounds, improving productivity while reducing traffic
Solution Approach 2:
The patent performs preliminary analysis of the probability distribution of model weights before compression. By pre-calculating the distribution characteristics and using them to guide scalar quantization and Huffman coding, the system optimizes compression efficiency in advance, enabling more effective communication with reduced traffic costs
Data Source
AI summary
A communication compression method based on model weight distribution in federated learning, and belongs to the technical field of wireless communication. Based on the existing federated average idea in federated learning, counts the distribution of model weight information to be transmitted between nodes during each communication, then performs scalar quantization and compression through Lloyd-Max quantizer according to their distribution characteristics, then encodes with Huffman coding method, and finally sends the codes to the target node, thereby the minimum mean square quantization error is realized and the number of bits required for communication is reduced.