Federated learning (FL) has received significant attention in recent years for its advantages in efficient training of machine learning models across distributed clients without disclosing user-sensitive data. Specifically, in federated edge learning (FEEL) systems, the time-varying nature of wireless channels introduces inevitable system dynamics in the communication process, thereby affecting training latency and energy consumption. In this work, we further consider a streaming data scenario where new training data samples are randomly generated over time at edge devices. Our goal is to develop a dynamic scheduling and resource allocation algorithm to address the inherent randomness in data arrivals and resource availability under long-term energy constraints. To achieve this, we formulate a stochastic network optimization problem and use the Lyapunov drift-plus-penalty framework to obtain a dynamic resource management design. Our proposed algorithm makes adaptive decisions on device scheduling, computational capacity adjustment, and allocation of bandwidth and transmit power in every round. We provide convergence analysis for the considered setting with heterogeneous data and time-varying objective functions, which supports the rationale behind our proposed scheduling design. The effectiveness of our scheme is verified through simulation results, demonstrating improved learning performance and energy efficiency as compared to baseline schemes.

翻译：联邦学习（FL）因其在分布式客户端上高效训练机器学习模型且无需暴露用户敏感数据的优势，近年来受到广泛关注。具体而言，在联邦边缘学习（FEEL）系统中，无线信道的时变特性在通信过程中引入了不可避免的系统动态性，进而影响训练延迟与能耗。本研究进一步考虑流数据场景，其中边缘设备端随时间随机生成新的训练数据样本。我们的目标是设计一种动态调度与资源分配算法，以应对数据到达与资源可用性的内在随机性，同时满足长期能量约束。为此，我们构建了一个随机网络优化问题，并利用李雅普诺夫漂移加罚框架实现动态资源管理设计。所提出的算法能够自适应决策每轮训练中的设备调度、计算能力调整以及带宽与发射功率的分配。我们针对异构数据与时变目标函数的场景进行了收敛性分析，这为所提出调度设计的合理性提供了理论支撑。通过仿真结果验证了该方案的有效性，表明与基准方案相比，该方案能够显著提升学习性能与能效。