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
this https URL

该研究提出了一种名为 ShapFed 的新型参与者贡献评估方法，利用合作博弈论中的 Shapley 值来对参与者的贡献进行精细化评估，并基于此提出了一种名为 ShapFed-WA 的加权聚合方法，尤其在类别不平衡的场景中表现优于传统的联邦平均方法，通过根据参与者的贡献个性化更新模型，进一步提高合作公平性。在 CIFAR-10、Chest X-Ray 和 Fed-ISIC2019 数据集上的实验证明了该方法在提高联邦学习系统的效用、效率和公平性方面的有效性。