FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification

arXiv:2404.15657v1 Announce Type: new
Abstract: While deep neural networks (DNNs) based personalized federated learning (PFL) is demanding for addressing data heterogeneity and shows promising performance, existing methods for federated learning (FL) suffer from efficient systematic uncertainty quantification. The Bayesian DNNs-based PFL is usually questioned of either over-simplified model structures or high computational and memory costs. In this paper, we introduce FedSI, a novel Bayesian DNNs-based subnetwork inference PFL framework. FedSI is simple and scalable by leveraging Bayesian methods to incorporate …

abstract arxiv bayesian computational cs.ai cs.lg data federated learning inference networks neural networks performance personalized quantification shows simplified type uncertainty while