通过分时冻结参数与功率控制，降低移动设备联邦学习能耗。
A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control
- 分两个时间尺度：稳定参数冻结，不稳定参数动态调功率。
- 在能量预算下将模型误差降低40%以上，收敛速度更快。
- 适合资源受限的移动设备，尤其适用于低功耗场景。
联邦学习（FL）使分布式设备在保护数据隐私的同时协同训练共享机器学习模型。然而，资源受限的移动设备面临模型参数带来的高计算与通信开销。本文观察到，在训练过程中模型参数往往在收敛前已趋于稳定。基于此，提出一种双时间尺度联邦学习框架，联合优化参数冻结与传输功率控制，以平衡能效与收敛性能。首先分析参数冻结与不可靠传输对收敛速率的影响；随后构建以最小化模型收敛误差为目标、满足能量预算的双时间尺度优化问题。采用李雅普诺夫优化方法将问题分解为并行子问题，并进一步分解为不同时间尺度求解，实现在线最优参数冻结与功率控制策略。实验结果表明，所提方案相比基准方法显著提升性能。
原文摘要 · Abstract (English)
Federated learning (FL) enables distributed devices to train a shared machine learning (ML) model collaboratively while protecting their data privacy. However, the resource-limited mobile devices suffer from intensive computation-and-communication costs of model parameters. In this paper, we observe the phenomenon that the model parameters tend to be stabilized long before convergence during training process. Based on this observation, we propose a two-timescale FL framework by joint optimization of freezing stabilized parameters and controlling transmit power for the unstable parameters to balance the energy consumption and convergence. First, we analyze the impact of model parameter freezing and unreliable transmission on the convergence rate. Next, we formulate a two-timescale optimization problem of parameter freezing percentage and transmit power to minimize the model convergence error subject to the energy budget. To solve this problem, we decompose it into parallel sub-problems and decompose each sub-problem into two different timescales problems using the Lyapunov optimization method. The optimal parameter freezing and power control strategies are derived in an online fashion. Experimental results demonstrate the superiority of the proposed scheme compared with the benchmark schemes.
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