Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation

TL;DR

This paper introduces QAFeL, a quantized asynchronous federated learning algorithm that reduces communication costs while maintaining high precision, supported by theoretical guarantees and experimental validation.

Contribution

The paper proposes QAFeL, a novel quantization scheme for asynchronous federated learning that prevents error propagation and improves communication efficiency.

Findings

QAFeL reduces communication costs significantly.

Theoretical convergence guarantees are established.

Experimental results validate the effectiveness of QAFeL.

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.