Instead of treating all federated learning clients equally, you can measure each client's actual impact on model improvement and weight their contributions accordingly—leading to faster convergence and better robustness to unreliable participants.
This paper introduces Trajectory Shapley Value (TSV), a fairness metric for federated learning that measures how much each client contributes to improving the global model over time.