A critical review of 121 federated learning fairness papers identifies five recurring pitfalls and proposes a harm-centered, lifecycle-based framework.
Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems
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abstract
Federated learning (FL) systems face performance challenges in dealing with heterogeneous devices and non-identically distributed data across clients. We propose a dynamic global model aggregation method within Asynchronous Federated Learning (AFL) deployments to address these issues. Our aggregation method scores and adjusts the weighting of client model updates based on their upload frequency to accommodate differences in device capabilities. Additionally, we also immediately provide an updated global model to clients after they upload their local models to reduce idle time and improve training efficiency. We evaluate our approach within an AFL deployment consisting of 10 simulated clients with heterogeneous compute constraints and non-IID data. The simulation results, using the FashionMNIST dataset, demonstrate over 10% and 19% improvement in global model accuracy compared to state-of-the-art methods PAPAYA and FedAsync, respectively. Our dynamic aggregation method allows reliable global model training despite limiting client resources and statistical data heterogeneity. This improves robustness and scalability for real-world FL deployments.
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Fairness in Federated Learning: Fairness for Whom?
A critical review of 121 federated learning fairness papers identifies five recurring pitfalls and proposes a harm-centered, lifecycle-based framework.