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.
no code implementations • 17 Feb 2023 • Qingxiang Liu, Sheng Sun, Min Liu, Yuwei Wang, Bo Gao
In this paper, we perform the first study of forecasting traffic flow adopting Online Learning (OL) manner in FL framework and then propose a novel prediction method named Online Spatio-Temporal Correlation-based Federated Learning (FedOSTC), aiming to guarantee performance gains regardless of traffic fluctuation.
2 code implementations • 14 Apr 2022 • Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu, Quyang Pan, Junbo Zhang, Zeju Li, Qingxiang Liu
Federated distillation (FD) is proposed to simultaneously address the above two problems, which exchanges knowledge between the server and clients, supporting heterogeneous local models while significantly reducing communication overhead.