  Federated learning (FL) has emerged as a pivotal approach in machine
learning, enabling multiple participants to collaboratively train a global
model without sharing raw data. While FL finds applications in various domains
such as healthcare and finance, it is challenging to ensure global model
convergence when participants do not contribute equally and/or honestly. To
overcome this challenge, principled mechanisms are required to evaluate the
contributions made by individual participants in the FL setting. Existing
solutions for contribution assessment rely on general accuracy evaluation,
often failing to capture nuanced dynamics and class-specific influences. This
paper proposes a novel contribution assessment method called ShapFed for
fine-grained evaluation of participant contributions in FL. Our approach uses
Shapley values from cooperative game theory to provide a granular understanding
of class-specific influences. Based on ShapFed, we introduce a weighted
aggregation method called ShapFed-WA, which outperforms conventional federated
averaging, especially in class-imbalanced scenarios. Personalizing participant
updates based on their contributions further enhances collaborative fairness by
delivering differentiated models commensurate with the participant
contributions. Experiments on CIFAR-10, Chest X-Ray, and Fed-ISIC2019 datasets
demonstrate the effectiveness of our approach in improving utility, efficiency,
and fairness in FL systems. The code can be found at
https://github.com/tnurbek/shapfed.
