Beyond performance-wise Contribution Evaluation in Federated Learning
This addresses the need for more comprehensive and equitable evaluation in federated learning, though it is incremental as it builds on existing Shapley value methods.
The paper tackles the problem of evaluating client contributions in federated learning beyond just performance metrics, focusing on trustworthiness dimensions like reliability, resilience, and fairness, and finds that no single client excels across all these independent dimensions.
Federated learning offers a privacy-friendly collaborative learning framework, yet its success, like any joint venture, hinges on the contributions of its participants. Existing client evaluation methods predominantly focus on model performance, such as accuracy or loss, which represents only one dimension of a machine learning model's overall utility. In contrast, this work investigates the critical, yet overlooked, issue of client contributions towards a model's trustworthiness -- specifically, its reliability (tolerance to noisy data), resilience (resistance to adversarial examples), and fairness (measured via demographic parity). To quantify these multifaceted contributions, we employ the state-of-the-art approximation of the Shapley value, a principled method for value attribution. Our results reveal that no single client excels across all dimensions, which are largely independent from each other, highlighting a critical flaw in current evaluation scheme: no single metric is adequate for comprehensive evaluation and equitable rewarding allocation.