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FedUVeQCS: Universal Vector Quantized Compressive Sensing for Communication-Efficient Federated Learning
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DOI：10.1109/JIOT.2024.3440959.png)
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Traditional machine learning involves collecting data from clients to a central server, where data may not be willingly shared by the clients. In contrast, federated learning (FL) trains a model without data sharing. The parameter server sends a global model to all clients, who train it locally and send local updates back. A major challenge is the high communication overhead from numerous local updates. To address this communication overhead, several algorithms have been proposed for FL tasks, such as sparsification and quantization. In this article, we propose a method called universal vector quantized compressive sensing for communication-efficient FL (FedUVeQCS). We demonstrate that combining universal vector quantized compressive sensing with FL enables dimensionality reduction and quantization of the local model updates without the need to consider the distribution of the reduced data. The quantization distortion caused by universal vector quantization can be considered as a negligible additive noise term. We evaluate FedUVeQCS on image classification tasks using MNIST and fashion-MNIST data sets and compare it with baseline algorithms, including FedAvg, universal vector quantization for FL, Top-k, and FedPAQ. Numerical results show the superiority of FedUVeQCS over baseline algorithms in terms of the number of bits uploaded while maintaining testing accuracy comparable to FedAvg.
Keyword:
Servers
Vector quantization
Vectors
Compressed sensing
Data models
Training
Internet of Things
Compressive sensing
federated learning (FL)
gradient compression
universal vector quantization
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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