{"type":"ScholarlyArticle","title":"Redefining Contributions: Shapley-Driven Federated Learning","authors":[{"name":"Nurbek Tastan","slug":"nurbek-tastan","given":"Nurbek","family":"Tastan","affiliation":"University of Artificial Intelligence (MBZUAI) , Abu Dhabi , UAE"},{"name":"Samar Fares","slug":"samar-fares","given":"Samar","family":"Fares","affiliation":"University of Artificial Intelligence (MBZUAI) , Abu Dhabi , UAE"},{"name":"Toluwani Aremu","slug":"toluwani-aremu","given":"Toluwani","family":"Aremu","affiliation":"University of Artificial Intelligence (MBZUAI) , Abu Dhabi , UAE"},{"name":"Samuel Horváth","slug":"samuel-horvath","given":"Samuel","family":"Horváth","affiliation":"University of Artificial Intelligence (MBZUAI) , Abu Dhabi , UAE"},{"name":"Karthik Nandakumar","slug":"karthik-nandakumar","given":"Karthik","family":"Nandakumar","affiliation":"University of Artificial Intelligence (MBZUAI) , Abu Dhabi , UAE"}],"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 \\href{https://github.com/tnurbek/shapfed}{https://github.com/tnurbek/shapfed}.","doi":"10.24963/ijcai.2024/554","url":"https://ijcai.org/proceedings/2024/554","pdf":"https://ijcai.org/proceedings/2024/0554.pdf","markdown":"https://ijcai.org/proceedings/2024/554.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/554","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":554,"pages":{"first":5009,"last":5017},"keywords":["AI Ethics, Trust, Fairness: ETF: Trustworthy AI","Computer Vision: CV: Bias, fairness and privacy","Machine Learning: ML: Federated learning","Machine Learning: ML: Trustworthy machine learning"],"videos":[],"references":[{"position":1,"raw":"[Gasanov et al., 2022] Elnur Gasanov, Ahmed Khaled, Samuel Horvath, and Peter Richtarik. 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