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.

通过动态调度和资源分配算法解决异构数据和时间变化目标函数下，在长期能量限制条件下，流式数据随机生成和资源可用性的固有随机性，并通过仿真结果验证了所提出方案相对于基准方案的学习性能和能效的提高。

基于流数据的能效联合边缘学习：一种 Lyapunov 优化方法

Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov  Optimization Approach

In this paper, we investigate the power of online learning in stochastic
network optimization with unknown system statistics {\it a priori}. We are
interested in understanding how information and learning can be efficiently
incorporated into system control techniques, and what are the fundamental
benefits of doing so. We propose two \emph{Online Learning-Aided Control}
techniques, $\mathtt{OLAC}$ and $\mathtt{OLAC2}$, that explicitly utilize the
past system information in current system control via a learning procedure
called \emph{dual learning}. We prove strong performance guarantees of the
proposed algorithms: $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ achieve the
near-optimal $[O(\epsilon), O([\log(1/\epsilon)]^2)]$ utility-delay tradeoff
and $\mathtt{OLAC2}$ possesses an $O(\epsilon^{-2/3})$ convergence time.
$\mathtt{OLAC}$ and $\mathtt{OLAC2}$ are probably the first algorithms that
simultaneously possess explicit near-optimal delay guarantee and sub-linear
convergence time. Simulation results also confirm the superior performance of
the proposed algorithms in practice. To the best of our knowledge, our attempt
is the first to explicitly incorporate online learning into stochastic network
optimization and to demonstrate its power in both theory and practice.

本文探讨了在未知系统统计数据的情况下，利用在线学习检测随机网络优化中的系统控制技术，证明了两个在线学习辅助控制技术 OLAC 和 OLAC2 的性能保证，并通过模拟结果证明了这些算法在实践中的优越性。