Performance Enhancement on Federated Learning Supported by RIS-Aided Communication in the FBL Regime

We consider a reconfigurable intelligent surface (RIS)-aided wireless network supporting local gradient upload for federated learning (FL). For the first time, the impact of wireless uploads with finite blocklength (FBL) on FL performance is investigated and provides a corresponding performance enhancement design. More specifically, we characterize the im-pact of wireless transmissions/uploads on the convergence and the optimality gap of FL, and formulate a resource allocation problem to minimize such impact accordingly. To tackle the formulated non-convex problem, we first conduct a convex approximation to the problem, then propose a block coordinate descent (BCD) based algorithm alternately optimizing the power allocation and RIS phase shifts via addressing two sub-problems. Specifically, we prove the convexity for the pure power allocation sub-problem, while for RIS phase design one, a closed-form expression of the optimal solution is derived by applying the path-following (PF) method. Numerical results demonstrate that the proposed design significantly improves the FL performance compared to baseline schemes.