Delving into Instances: A Unified Perspective for Fair Federated Learning

Fairness in federated learning (FL) is commonly studied at the client or group level to reduce performance disparities across clients or groups. Such coarse-grained views overlook an important source of unfairness: *similar instances may receive inconsistent predictions, even within the same client*. In this work, we find that *instances provide a unified and finer-grained perspective for fairness in FL*. Instance-level prediction consistency not only characterizes *intra-client unfairness* among similar instances within a client, but also provides a basis for identifying and mitigating *inter-client unfairness* through differences in the consistency of instances across clients. Based on this insight, we propose Federated Instance-Consistency Intervention Learning (FedICIL), a unified instance-level framework for fair FL. Within each client, FedICIL constructs intra-client neighborhoods according to instance similarity and explicitly regularizes predictions to be consistent within each neighborhood. To make the intervention more targeted under limited local update budgets, FedICIL further identifies parameters that are sensitive to instance-level prediction inconsistency and selectively updates them. Beyond the client level, clients communicate a lightweight instance-consistency signal, which is used to adapt aggregation weights according to the remaining inconsistency of their local predictions. In this way, the same instance-level signal is exploited to address both intra-client inconsistency and inter-client performance disparities. Experiments on diverse benchmarks demonstrate that FedICIL achieves a better trade-off among predictive performance, inter-client fairness, intra-client fairness, and group fairness compared with existing methods.