  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.
