Cumulative Utility Parity: Fair Learning under Intermittent Client Participation

Intermittent and heterogeneous client availability creates unequal opportunities to contribute to federated learning, a disparity that snapshot metrics for loss and accuracy do not capture and completely miss this unfairness. We introduce cumulative utility parity (CUP), which evaluates each client's accumulated training utility relative to its observed availability. CUP prioritizes clients with larger availability-normalized utility deficits and uses cached model updates as surrogates for unavailable clients. We characterize availability-normalized utility disparity, derive a max–min allocation target, and bound the discrepancy between cached surrogates and fresh updates. Across non-IID CIFAR-10 experiments with two and five labels per client, CUP without caching achieves lower mean temporal utility dispersion than all evaluated baselines in the two-label setting and than FedProx, q-FFL, AFL, and FairFedCS in the five-label setting. The cached CUP variants achieve the lowest mean CV and highest mean Jain index in both settings, together with the highest mean final accuracy in the five-label setting. These findings highlight the potential of availability-aware utility allocation to improve cumulative fairness alongside predictive performance.