FedHP: Federated Learning with Hyperspherical Prototypical Regularization
Samuele Fonio, Mirko Polato, Roberto Esposito · 2024
This paper presents FedHP, an algorithm that amalgamates federated learning, hyperspherical geometries, and prototype learning.Federated Learning (FL) has garnered attention as a privacy-preserving method for constructing robust models across distributed datasets.Traditionally, FL involves exchanging model parameters to uphold data privacy; however, in scenarios with costly data communication, exchanging large neural network models becomes impractical.In such instances, prototype learning provides a feasible solution by necessitating the exchange of a few class prototypes instead of entire deep learning models.Motivated by these considerations, our approach leverages recent advancements in prototype learning, particularly the benefits offered by non-Euclidean geometries.Alongside introducing FedHP, we provide empirical evidence demonstrating its comparable performance to other state-of-the-art approaches while significantly reducing communication costs.