Redefining Contributions: Shapley-Driven Federated Learning
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.
Code (1)
Tasks
Collaborative FairnessContribution AssessmentData ValuationFairnessFederated LearningSimilar Papers 제목 키워드 기반
Measuring Participant Contributions in Decentralized Federated Learning
Federated learning (FL) enables multiple clients to collaboratively train models without sharing their data. Measuring participant contributions in FL is crucial for incentivizing clients and ensuring transparency. While…
Federated LearningIn-Run Data Shapley for Adam Optimizer
Reliable data attribution is essential for mitigating bias and reducing computational waste in modern machine learning, with the Shapley value serving as the theoretical gold standard. While recent "In-Run" methods bypas…
Improving Fairness for Data Valuation in Horizontal Federated Learning
Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federated learning depends largely on the parti…
Data ValuationFairnessFederated LearningMitigating federated learning contribution allocation instability through randomized aggregation
Federated learning (FL) is a collaborative and privacy-preserving Machine Learning paradigm, allowing the development of robust models without the need to centralise sensitive data. A critical challenge in FL lies in fai…
FairnessFederated LearningPrivacy PreservingDPVS-Shapley:Faster and Universal Contribution Evaluation Component in Federated Learning
In the current era of artificial intelligence, federated learning has emerged as a novel approach to addressing data privacy concerns inherent in centralized learning paradigms. This decentralized learning model not only…
Contribution AssessmentFederated Learning