Paper Proposes Mechanism-Architecture Co-Design for Client Participation in Federated Learning

A new arXiv preprint examines individual rationality in federated learning, where clients weigh privacy, communication, and computation costs against possible model quality gains. The authors introduce a pathwise notion of individual rationality and co-design incentive mechanisms together with system architecture. The work aims to keep participation worthwhile for clients while preserving training outcomes.