Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems

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 …

aggregation asynchronous bias challenges client cs.lg data deployments devices distributed distributed data dynamic face federated learning global learning systems performance systems updates