Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation

Tomas Ortega; Hamid Jafarkhani

Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation

Machine Learning 2023-08-02 v1 Signal Processing Optimization and Control

Authors: Tomas Ortega , Hamid Jafarkhani

Abstract

Asynchronous Federated Learning with Buffered Aggregation (FedBuff) is a state-of-the-art algorithm known for its efficiency and high scalability. However, it has a high communication cost, which has not been examined with quantized communications. To tackle this problem, we present a new algorithm (QAFeL), with a quantization scheme that establishes a shared "hidden" state between the server and clients to avoid the error propagation caused by direct quantization. This approach allows for high precision while significantly reducing the data transmitted during client-server interactions. We provide theoretical convergence guarantees for QAFeL and corroborate our analysis with experiments on a standard benchmark.

Keywords

federated learning nonnegative matrix factorization

Cite

@article{arxiv.2308.00263,
  title  = {Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation},
  author = {Tomas Ortega and Hamid Jafarkhani},
  journal= {arXiv preprint arXiv:2308.00263},
  year   = {2023}
}

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Accepted at the 2023 ICML Workshop of Federated Learning and Analytics in Practice

Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation

Abstract

Keywords

Cite

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