  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.
