  We address the relatively unexplored problem of hyper-parameter optimization
(HPO) for federated learning (FL-HPO). We introduce Federated Loss suRface
Aggregation (FLoRA), the first FL-HPO solution framework that can address use
cases of tabular data and gradient boosting training algorithms in addition to
stochastic gradient descent/neural networks commonly addressed in the FL
literature. The framework enables single-shot FL-HPO, by first identifying a
good set of hyper-parameters that are used in a **single** FL training. Thus,
it enables FL-HPO solutions with minimal additional communication overhead
compared to FL training without HPO. Our empirical evaluation of FLoRA for
Gradient Boosted Decision Trees on seven OpenML data sets demonstrates
significant model accuracy improvements over the considered baseline, and
robustness to increasing number of parties involved in FL-HPO training.
