Federated Learning (FL) allows clients to train a model collaboratively
without sharing their private data. One key challenge in practical FL systems
is data heterogeneity, particularly in handling clients with rare data, also
referred to as Mavericks. These clients own one or more data classes
exclusively, and the model performance becomes poor without their
participation. Thus, utilizing Mavericks throughout training is crucial. In
this paper, we first design a Maverick-aware Shapley valuation that fairly
evaluates the contribution of Mavericks. The main idea is to compute the
clients' Shapley values (SV) class-wise, i.e., per label. Next, we propose
FedMS, a Maverick-Shapley client selection mechanism for FL that intelligently
selects the clients that contribute the most in each round, by employing our
Maverick-aware SV-based contribution score. We show that, compared to an
extensive list of baselines, FedMS achieves better model performance and fairer
Shapley Rewards distribution.

使用 Federated Learning 进行模型训练时，处理数据异质性和 Mavericks 等罕见数据的挑战很重要。本文提出了一种考虑 Mavericks 的 Shapley 估值方法，并利用 Maverick-aware SV-based contribution score 在每轮中智能选择对模型最有贡献的客户端，从而实现更好的模型性能和公平的 Shapley Rewards 分配。