Straggler Effect Mitigation for Federated Learning in Cell-Free Massive MIMO

Straggler effect is the main bottleneck in realizing federated learning (FL) in wireless networks. This work proposes a novel user (UE) selection approach to mitigate this effect with UE sampling in cell-free massive multiple-input multiple-output networks. Our proposed approach selects only a small subset of UEs for participating in one FL process. Importantly, since the UEs are selected before any FL process is executed, the performance of FL during the executing time is not affected by our method. Here, we select UEs by solving an FL transmission time minimization problem that jointly optimizes UE selection, power control, and data rate. The problem is formulated to capture the complex interactions among the FL training time, UE selection, and straggler effect. This mixed-integer mixed-timescale stochastic nonconvex problem is constrained by the minimum number of UEs to guarantee the quality of learning. By employing online successive convex approximation, we propose a novel algorithm to solve the formulated problem with guaranteed convergence to the neighbourhood of their stationary points. Our approach can significantly reduce the FL transmission time over baseline approaches, especially in the networks that experience serious straggler effect due to the moderately low density of access points.