Don't Forget What I did?: Assessing Client Contributions in Federated Learning

arXiv:2403.07151v1 Announce Type: cross
Abstract: Federated Learning (FL) is a collaborative machine learning (ML) approach, where multiple clients participate in training an ML model without exposing the private data. Fair and accurate assessment of client contributions is an important problem in FL to facilitate incentive allocation and encouraging diverse clients to participate in a unified model training. Existing methods for assessing client contribution adopts co-operative game-theoretic concepts, such as Shapley values, but under simplified assumptions. In this paper, we propose …

arxiv assessment client clients cs.ai cs.cr cs.lg data don exposing fair federated federated learning important machine machine learning ml model private private data problem training