Beyond performance-wise Contribution Evaluation in Federated Learning
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), […]