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.
@inproceedings{tastan2024redefining,author={Tastan, Nurbek and Fares, Samar and Aremu, Toluwani and Horvath, Samuel and Nandakumar, Karthik},title={Redefining Contributions: Shapley-Driven Federated Learning},booktitle={International Joint Conference on Artificial Intelligence (IJCAI)},year={2024},month=aug,}
@inproceedings{al2024collaborative,title={Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline},author={Al-lahham, Anas and Zaheer, Muhammad Zaigham and Tastan, Nurbek and Nandakumar, Karthik},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},year={2024},month=jun,pages={12416-12425},}
@inproceedings{al2024coarse,title={A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection},author={Al-lahham, Anas and Tastan, Nurbek and Zaheer, Muhammad Zaigham and Nandakumar, Karthik},booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},pages={6793--6802},year={2024},month=jan,}
@inproceedings{tastan2023capride,author={Tastan, Nurbek and Nandakumar, Karthik},title={CaPriDe Learning: Confidential and Private Decentralized Learning Based on Encryption-Friendly Distillation Loss},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month=jun,year={2023},pages={8084-8092},}
@inproceedings{amanzholova2020valid,title={Valid and Invalid Bitcoin Transactions},author={Amanzholova, Saule and Tastan, Nurbek and Kalkamanova, Kamila and Yessenalina, Amina},booktitle={Proceedings of the 6th International Conference on Engineering \& MIS 2020},pages={1--5},year={2020},}
@inproceedings{tastan2019burglary,title={Burglary Detection Framework for House Crime Control},author={Tastan, Nurbek and Razaque, Abdul and Frej, Mohamed Ben Haj and Toksanovna, Amanzholova Saule and Ganda, Raouf M and Amsaad, Fathi},booktitle={2019 19th International Conference on Computational Science and Its Applications (ICCSA)},pages={152--157},year={2019},organization={IEEE},}
