FedBEns: One-Shot Federated Learning based on Bayesian Ensemble

Several One-Shot FL algorithms have been proposed in the literature. Existing relevant work leverages knowledge distillation One-Shot Federated Learning (FL) is a recent at the server (Lin et al., 2020), neuron matching paradigm that enables multiple clients to cooperatively strategies (Singh & Jaggi, 2020) or adopts an optimization learn a global model in a single round of approach, trying to directly approximate the global loss at communication with a central server. In this paper, the server starting from the local losses of each client (Jhunjhunwala we analyze the One-Shot FL problem through the et al., 2024; Liu et al., 2024; Matena & Raffel, lens of Bayesian inference and propose FedBEns, 2022). Our contribution is in line with the last group of work, an algorithm that leverages the inherent multimodality which generally employs a unimodal approximation of each of local loss functions to find better local loss. As an example, Jhunjhunwala et al. (2024) make global models.