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.

翻译：异步缓冲聚合联邦学习（FedBuff）是一种以高效性和高可扩展性著称的最先进算法。然而，其通信成本较高，且尚未在量化通信场景下得到充分研究。为解决此问题，我们提出一种新算法（QAFeL），该算法采用量化方案在服务器与客户端之间建立共享的“隐”状态，从而避免直接量化导致的误差传播。该方法能够在显著减少客户端-服务器交互过程中数据传输量的同时保持高精度。我们为QAFeL提供了理论收敛性保证，并通过标准基准实验验证了分析结果。