Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems
This addresses system bias and improves robustness for real-world federated learning deployments, but it is incremental as it builds on existing asynchronous FL methods.
The paper tackled performance challenges in asynchronous federated learning systems with heterogeneous devices and non-IID data by proposing a dynamic global model aggregation method that adjusts client update weights based on upload frequency and reduces idle time. The result showed over 10% and 19% improvement in global model accuracy compared to state-of-the-art methods PAPAYA and FedAsync on the FashionMNIST dataset.
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.