  In this work, we explore combining automatic hyperparameter tuning and
optimization for federated learning (FL) in an online, one-shot procedure. We
apply a principled approach on a method for adaptive client learning rate,
number of local steps, and batch size. In our federated learning applications,
our primary motivations are minimizing communication budget as well as local
computational resources in the training pipeline. Conventionally,
hyperparameter tuning methods involve at least some degree of trial-and-error,
which is known to be sample inefficient. In order to address our motivations,
we propose FATHOM (Federated AuTomatic Hyperparameter OptiMization) as a
one-shot online procedure. We investigate the challenges and solutions of
deriving analytical gradients with respect to the hyperparameters of interest.
Our approach is inspired by the fact that, with the exception of local data, we
have full knowledge of all components involved in our training process, and
this fact can be exploited in our algorithm impactfully. We show that FATHOM is
more communication efficient than Federated Averaging (FedAvg) with optimized,
static valued hyperparameters, and is also more computationally efficient
overall. As a communication efficient, one-shot online procedure, FATHOM solves
the bottleneck of costly communication and limited local computation, by
eliminating a potentially wasteful tuning process, and by optimizing the
hyperparamters adaptively throughout the training procedure without
trial-and-error. We show our numerical results through extensive empirical
experiments with the Federated EMNIST-62 (FEMNIST) and Federated Stack Overflow
(FSO) datasets, using FedJAX as our baseline framework.
