Who Trains Matters: Federated Learning under Enrollment and Participation Selection Biases

ArXi:2604.26604v1 Announce Type: new Federated learning (FL) trains a shared model from updates contributed by distributed clients, often implicitly assuming that contributing clients are representative of the target population. In practice, this representativeness assumption can fail at two distinct stages, inducing selection bias. First, eligibility rules such as device constraints, software requirements, or user consent determine which clients are ever enrolled and reachable for