Poster
in
Workshop: Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities
Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computation
Anbang Zhang · Shuaishuai Guo · Shuai Liu
To further preserve model weight privacy and improve model performance in Federated Learning (FL), FL via Over-the-Air Computation (AirComp) scheme based on dynamic power control is proposed. The edge devices transmit the signs of local stochastic gradients by activating two adjacent orthogonal frequency division multiplexing (OFDM) subcarriers, and majority votes (MVs) at the edge server are obtained by exploiting the energy accumulation on the subcarriers. Then, we propose a dynamic power control algorithm to further offset the biased aggregation of MV aggregation values. We show that the whole scheme can mitigate the impact of the time synchronization error, channel fading, and noise. The convergence of the scheme is theoretically proved.