Personalized Federated Learning with Adaptive Bitwidth Quantization
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Solution Overview
Problem
Federated learning systems face challenges due to communication costs, heterogeneity in client resources and data, and privacy concerns, leading to inefficient model training and accuracy issues in vehicular applications.
Innovation Solution
Implement personalized federated learning using non-uniform quantization and self-supervised learning (SSL) to de-quantize and re-quantize models based on global unlabeled data, aggregating them to maintain accuracy and reduce communication bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning transmits model parameters between server and edge nodes, then distributed model training is enabled, but communication cost becomes very high
Solution Approach 1:
The patent applies quantization to change the parameter representation of model weights from high-precision floating-point numbers to low-precision discrete values. This parameter transformation significantly reduces the communication bandwidth required while maintaining acceptable model training capability and accuracy.
Solution Approach 2:
The patent extracts only the essential information from full-precision model parameters by applying quantization, transmitting only the quantized parameters to edge nodes. This extraction process reduces communication overhead while preserving the core learning capabilities needed for distributed training.
2Stability of the object's composition
If all edge nodes train models with the same infrastructure resource, then model consistency is maintained, but slower nodes become stragglers that increase total training time
Solution Approach 1:
The patent implements dynamic bitwidth assignment where different edge nodes can use different quantization bitwidths based on their computational capabilities. This dynamic adaptation allows powerful nodes to use lower bitwidth for faster training while less powerful nodes can use higher bitwidth, eliminating the straggler effect while maintaining system flexibility.
Solution Approach 2:
The patent allows each edge node to have customized model parameters with different bitwidth configurations according to its local resource characteristics. This local quality differentiation enables each node to operate at its optimal performance point without being constrained by the capabilities of other nodes in the federation.
3Loss of energy
If quantized models with different bitwidth are used by different client devices, then communication efficiency is improved, but model aggregation becomes complex
Solution Approach 1:
The patent introduces the server as an intermediary that performs de-quantization of received quantized models from different edge nodes. By converting all quantized models to a common high-precision format before aggregation, the server mediates the complexity of handling different bitwidths, allowing edge nodes to communicate efficiently with low-precision parameters while the server manages the aggregation complexity centrally.
Data Source
AI summary
Provided is a system for personalized federated learning under bitwidth for client resource and data heterogeneity. The server includes one or more processors programmed to obtain quantized models under different bitwidth generated by client devices, de-quantize the quantized models by using global unlabeled data to run self-supervised learning (SSL), aggregate the de-quantized models, re-quantize the aggregated models based on the SSL and the global unlabeled data, and transmit the re-quantized models to the client devices.