FUSE: Fairness-aware Update Screening against Byzantine Exploitation in $q$-Fair Federated Learning.

arXiv:2610.20264v1 Announce Type: cross Abstract: Client-level fair federated learning, in the form of $q$-fair federated learning ($q$-FFL), agnostic federated learning, or tilted empirical risk minimization, has emerged as an attractive solution to equalize