论文标题
美联储：智能选择的联合学习
FedSS: Federated Learning with Smart Selection of clients
论文作者
论文摘要
联合学习提供了以分布式方式学习异质用户数据的能力，同时保留用户隐私。但是，当前的客户选择技术是偏见的来源，因为它可以区分慢速客户。对于初学者来说，它选择满足某些网络和系统特定标准的客户端，从而选择慢速客户端。即使将这些客户包括在培训过程中，他们要么在培训中挣扎，要么因为太慢而被完全丢弃。我们提出的想法希望通过查看智能客户选择和调度技术来找到快速收敛和异质性之间的最佳位置。
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.
