  The newly emerging federated learning (FL) framework offers a new way to
train machine learning models in a privacy-preserving manner. However,
traditional FL algorithms are based on an event-triggered aggregation, which
suffers from stragglers and communication overhead issues. To address these
issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over
wireless networks, which is a generalized form of classic synchronous and
asynchronous FL. Taking the constrained resource and unreliable nature of
wireless communication into account, we jointly study the user selection and
bandwidth optimization problem to minimize the FL training loss. To solve this
joint optimization problem, we provide a thorough convergence analysis for
TT-Fed. Based on the obtained analytical convergence upper bound, the
optimization problem is decomposed into tractable sub-problems with respect to
each global aggregation round, and finally solved by our proposed online search
algorithm. Simulation results show that compared to asynchronous FL (FedAsync)
and FL with asynchronous user tiers (FedAT) benchmarks, our proposed TT-Fed
algorithm improves the converged test accuracy by up to 12.5% and 5%,
respectively, under highly imbalanced and non-IID data, while substantially
reducing the communication overhead.
