Federated learning (FL) is a new distributed machine learning framework known for its benefits on data privacy and communication efficiency. Since full client participation in many cases is infeasible due to constrained resources, partial participation FL algorithms have been investigated that proactively select/sample a subset of clients, aiming to achieve learning performance close to the full participation case. This paper studies a passive partial client participation scenario that is much less well understood, where partial participation is a result of external events, namely client dropout, rather than a decision of the FL algorithm. We cast FL with client dropout as a special case of a larger class of FL problems where clients can submit substitute (possibly inaccurate) local model updates. Based on our convergence analysis, we develop a new algorithm FL-FDMS that discovers friends of clients (i.e., clients whose data distributions are similar) on-the-fly and uses friends' local updates as substitutes for the dropout clients, thereby reducing the substitution error and improving the convergence performance. A complexity reduction mechanism is also incorporated into FL-FDMS, making it both theoretically sound and practically useful. Experiments on MNIST and CIFAR-10 confirmed the superior performance of FL-FDMS in handling client dropout in FL.

翻译：联邦学习（FL）是一种新兴的分布式机器学习框架，因其在数据隐私和通信效率方面的优势而闻名。由于资源受限导致在多数情况下难以实现全客户端参与，研究者们已提出部分参与FL算法，通过主动选择/采样部分客户端子集，旨在实现接近全参与情况的学习性能。本文研究了一种理解尚不充分的被动部分客户端参与场景，其中部分参与是由外部事件（即客户端丢失）所致，而非FL算法的决策。我们将存在客户端丢失的FL视为更广泛一类FL问题的特例，在该类问题中客户端可提交替代性（可能不准确的）局部模型更新。基于收敛性分析，我们提出了一种新算法FL-FDMS，该算法能动态发现客户端的"朋友"（即数据分布相似的客户端），并利用朋友提出的局部更新作为丢失客户端的替代，从而减少替代误差并提升收敛性能。FL-FDMS还集成了复杂度降低机制，使其兼具理论合理性与实际应用价值。在MNIST和CIFAR-10上的实验证实了FL-FDMS在处理FL中客户端丢失问题时的优越性能。