The enormous amount of data produced by mobile and IoT devices has motivated the development of federated learning (FL), a framework allowing such devices (or clients) to collaboratively train machine learning models without sharing their local data. FL algorithms (like FedAvg) iteratively aggregate model updates computed by clients on their own datasets. Clients may exhibit different levels of participation, often correlated over time and with other clients. This paper presents the first convergence analysis for a FedAvg-like FL algorithm under heterogeneous and correlated client availability. Our analysis highlights how correlation adversely affects the algorithm's convergence rate and how the aggregation strategy can alleviate this effect at the cost of steering training toward a biased model. Guided by the theoretical analysis, we propose CA-Fed, a new FL algorithm that tries to balance the conflicting goals of maximizing convergence speed and minimizing model bias. To this purpose, CA-Fed dynamically adapts the weight given to each client and may ignore clients with low availability and large correlation. Our experimental results show that CA-Fed achieves higher time-average accuracy and a lower standard deviation than state-of-the-art AdaFed and F3AST, both on synthetic and real datasets.

本文提出了第一个针对不同客户的异构和相关可用性的FedAvg类FL算法的收敛分析，发现相关性如何不利地影响算法的收敛速率，并且介绍了一种新的算法CA-Fed，在最大化收敛速度和最小化模型偏差之间寻求平衡，同时忽略了可用性低和相关性大的客户。实验结果表明，CA-Fed在合成数据集和实际数据集上比AdaFed和F3AST等现有算法具有更高的时间平均准确性和更低的标准差。

异构和相关客户端的联邦学习