Redefining Contributions: Shapley-Driven Federated Learning

TL;DR

This paper introduces ShapFed, a contribution assessment method using Shapley values for federated learning, improving fairness and performance especially in class-imbalanced scenarios.

Contribution

It proposes a novel Shapley value-based contribution evaluation method for federated learning, enhancing fairness and model personalization.

Findings

ShapFed outperforms traditional federated averaging in class-imbalanced settings.

Personalized updates based on contributions improve fairness and utility.

Experiments on multiple datasets validate the effectiveness of the approach.

Abstract

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.