Robust Federated Learning Over Noisy Fading Channels

The performance capabilities of models trained in a federated learning (FL) setting over wireless networks can be significantly affected by the underlying properties of the transmission channel. Even for shallow models, there can be an acute degradation in performance which necessitates the development of algorithms which are robust to transmission channel effects, such as noise and fading. In this work, we present a two-pronged approach to overcome the limitations of existing wireless machine learning (ML)-based algorithms. First, to tackle the effect of channel noise, we incorporate a novel tracking-based stochastic approximation scheme in the standard federated averaging pipeline which averages out the effect of the channel noise. In contrast to previous works on FL with a noisy channel, we provide exact convergence guarantees for our algorithm without the need to increase the transmission power gain. Second, to combat channel fading and further optimize the power consumption at the client level, we propose an adaptive transmission policy obtained by solving an optimization problem with long-term constraints. The solution is obtained in an online manner via a dual decomposition method. The superior empirical performance of the proposed scheme compared to state-of-the-art works is demonstrated on standard ML tasks.