Federated learning provides the ability to learn over heterogeneous user data in a distributed manner while preserving user privacy. However, its current client selection technique is a source of bias as it discriminates against slow clients. For starters, it selects clients that satisfy certain network and system-specific criteria, thus not selecting slow clients. Even when such clients are included in the training process, they either struggle with the training or are dropped altogether for being too slow. Our proposed idea looks to find a sweet spot between fast convergence and heterogeneity by looking at smart client selection and scheduling techniques.

翻译：摘要：联邦学习能够在分布式环境下异构用户数据上进行学习，同时保护用户隐私。然而，其当前的客户端选择技术存在偏差，因为它会歧视慢速客户端。首先，该技术会选择满足特定网络和系统标准的客户端，从而不选择慢速客户端。即使这些客户端被纳入训练过程，它们要么在训练中遇到困难，要么因速度过慢而被完全丢弃。我们提出的方案旨在通过智能客户端选择与调度技术，在快速收敛与异构性之间寻求平衡点。