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Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy
In this paper, we propose a novel wireless FL scheme called private federated edge learning with sparsification (PFELS) to provide client-level DP guarantee with intrinsic channel noise while reducing communication and energy overhead and improving model accuracy.
Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking
On the other hand, the edge-based FL framework that relies on an edge server co-located with mobile base station for model aggregation has low communication latency but suffers from degraded model accuracy due to the limited coverage of edge server.
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