  Federated learning (FL) enables a set of client devices to collaboratively
train a model without sharing raw data. This process, though, operates under
the constrained computation and communication resources of edge devices. These
constraints combined with systems heterogeneity force some participating
clients to perform fewer local updates than expected by the server, thus
slowing down convergence. Exhaustive tuning of hyperparameters in FL,
furthermore, can be resource-intensive, without which the convergence is
adversely affected. In this work, we propose GeL, the guess and learn
algorithm. GeL enables constrained edge devices to perform additional learning
through guessed updates on top of gradient-based steps. These guesses are
gradientless, i.e., participating clients leverage them for free. Our generic
guessing algorithm (i) can be flexibly combined with several state-of-the-art
algorithms including FedProx, FedNova or FedYogi; and (ii) achieves
significantly improved performance when the learning rates are not best tuned.
We conduct extensive experiments and show that GeL can boost empirical
convergence by up to 40% in resource-constrained networks while relieving the
need for exhaustive learning rate tuning.
