Howard H. Yang

TL;DR: Wang et al. as mentioned in this paper proposed a novel framework based on the concept of differential privacy, in which artificial noise is added to parameters at the clients' side before aggregating, namely, noising before model aggregation FL (NbAFL).

TL;DR: A novel framework based on the concept of differential privacy, in which artificial noise is added to parameters at the clients’ side before aggregating, namely, noising before model aggregation FL (NbAFL), is proposed and an optimal convergence bound is found that achieves the best convergence performance at a fixed privacy level.

TL;DR: In this article, an analytical model is developed to characterize the performance of FL in wireless networks, accounting for effects from both scheduling schemes and inter-cell interference, and it is shown that running FL with PF outperforms RS and RR if the network is operating under a high signal-to-interference-plus-noise ratio (SINR) threshold, while RR is more preferable when the SINR threshold is low.

TL;DR: An analytical model is developed to characterize the performance of federated learning in wireless networks and shows that running FL with PF outperforms RS and RR if the network is operating under a high signal-to-interference-plus-noise ratio (SINR) threshold, while RR is more preferable when the SINR threshold is low.

TL;DR: It is shown that having a high EH efficiency enhances the performance of D2D-EHHN, but can also degrade, especially for dense network, and the effects of network parameters on the outage probability and the optimal offloading bias in terms of the outage probabilities are explored.