AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory
This addresses communication inefficiencies in federated learning for distributed systems, offering a novel solution to deep fading, though it is incremental as it builds on over-the-air FL.
The paper tackles the communication bottleneck in federated learning under deep fading conditions by proposing AirFL-Mem, a scheme with a long-term memory mechanism, achieving the same convergence rate as ideal communication and eliminating error floors found in existing methods.
Addressing the communication bottleneck inherent in federated learning (FL), over-the-air FL (AirFL) has emerged as a promising solution, which is, however, hampered by deep fading conditions. In this paper, we propose AirFL-Mem, a novel scheme designed to mitigate the impact of deep fading by implementing a \emph{long-term} memory mechanism. Convergence bounds are provided that account for long-term memory, as well as for existing AirFL variants with short-term memory, for general non-convex objectives. The theory demonstrates that AirFL-Mem exhibits the same convergence rate of federated averaging (FedAvg) with ideal communication, while the performance of existing schemes is generally limited by error floors. The theoretical results are also leveraged to propose a novel convex optimization strategy for the truncation threshold used for power control in the presence of Rayleigh fading channels. Experimental results validate the analysis, confirming the advantages of a long-term memory mechanism for the mitigation of deep fading.