Priority-Aware Transmission for Federated Learning Over Wireless Networks

Unreliable communication is a critical bottleneck for the performance of federated learning (FL) in resourceconstrained wireless networks. To address this issue, we propose a priority-aware transmission strategy, where wireless resources are allocated preferentially based on the importance of data. Specifically, recognizing the crucial role of gradient direction in model updating, we transmit the sign and the modulus of local gradients separately, enabling the reuse of sign packets in the event of erroneous modulus transmission. Furthermore, we introduce a hierarchical resource allocation strategy in the proposed framework, prioritizing key gradients via bandwidth allocation across devices and the sign packet via power allocation at each device. Building upon the theoretical one-step convergence analysis, we formulate the resource allocation optimization problem in an explicit form, which facilitates an alternating optimization algorithm respectively applying the Newton method and technique of successive convex approximation (SCA). Numerical results show the superiority of the proposed scheme in both accuracy and convergence rate compared to existing baselines.