FedTSV

FedTSV: Fairness-Aware Federated Learning via Trajectory Shapley Value

This paper introduces the Trajectory Shapley Value (TSV), a contribution metric that evaluates each federated learning client's influence on the global model's optimization trajectory using validation-based, temporally consistent utility. Building on TSV, the authors propose FedTSV, an adaptive aggregation method that converts per-round evaluations into dynamic client weights to handle heterogeneous and adversarial participation. Experiments on benchmark datasets demonstrate improved convergence speed, robustness, and equitable contribution assessment compared to fixed-weight aggregation baselines.