FedKT: A Nature-Inspired Bayesian Federated Learning Framework for Privacy-Preserving and Lightweight Consumer Devices

Federated learning (FL) enables collaborative model training across distributed clients without sharing local data, providing an effective paradigm for privacy-preserving learning. To equip FL with principled uncertainty quantification, Bayesian neural networks have been introduced into federated settings. However, existing Bayesian FL frameworks typically rely on dense posterior parameterization or stochastic sampling methods, which lead to high computational and communication overhead and limit their scalability. In this paper, we propose FedKT, a nature-inspired Bayesian federated learning framework that introduces the k-tied parameterization into mean-field variational inference to achieve compact and…