  In this paper, we address the problem of privacy-preserving hyperparameter
(HP) tuning for cross-silo federated learning (FL). We first perform a
comprehensive measurement study that benchmarks various HP strategies suitable
for FL. Our benchmarks show that the optimal parameters of the FL server, e.g.,
the learning rate, can be accurately and efficiently tuned based on the HPs
found by each client on its local data. We demonstrate that HP averaging is
suitable for iid settings, while density-based clustering can uncover the
optimal set of parameters in non-iid ones. Then, to prevent information leakage
from the exchange of the clients' local HPs, we design and implement PrivTuna,
a novel framework for privacy-preserving HP tuning using multiparty homomorphic
encryption. We use PrivTuna to implement privacy-preserving federated averaging
and density-based clustering, and we experimentally evaluate its performance
demonstrating its computation/communication efficiency and its precision in
tuning hyperparameters.
