On five physical edge devices, asynchronous federated learning reached 75% accuracy about ten times faster than synchronous learning, but high-end devices supplied far more updates and accumulated up to roughly five times more privacy loss, while slow devices lost more accuracy under local…
Balancing privacy and performance in federated learning: a systematic literature review on methods and metrics.Journal of Parallel and Distributed Computing, page 104918, 2024
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Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs
On five physical edge devices, asynchronous federated learning reached 75% accuracy about ten times faster than synchronous learning, but high-end devices supplied far more updates and accumulated up to roughly five times more privacy loss, while slow devices lost more accuracy under local…