Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach

no code implementations • 4 Apr 2024 • Qingxiang Liu, Sheng Sun, Yuxuan Liang, Jingjing Xue, Min Liu

From spatial perspective, we design lightweight-but-efficient prototypes as client-level semantic representations, based on which the server evaluates spatial similarity and yields client-customized global prototypes for the supplemented inter-client contrastive task.

Contrastive Learning Personalized Federated Learning +3

FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout

no code implementations • 14 Jul 2023 • Jingjing Xue, Min Liu, Sheng Sun, Yuwei Wang, Hui Jiang, Xuefeng Jiang

In this paper, we propose Federated learning with Bayesian Inference-based Adaptive Dropout (FedBIAD), which regards weight rows of local models as probability distributions and adaptively drops partial weight rows based on importance indicators correlated with the trend of local training loss.

Bayesian Inference Federated Learning +1