Energizing Federated Learning via Filter-Aware Attention

no code implementations • 18 Nov 2023 • Ziyuan Yang, Zerui Shao, Huijie Huangfu, Hui Yu, Andrew Beng Jin Teoh, Xiaoxiao Li, Hongming Shan, Yi Zhang

Federated learning (FL) is a promising distributed paradigm, eliminating the need for data sharing but facing challenges from data heterogeneity.

Federated Learning

FedCliP: Federated Learning with Client Pruning

no code implementations • 17 Jan 2023 • Beibei Li, Zerui Shao, Ao Liu, Peiran Wang

The prevalent communication efficient federated learning (FL) frameworks usually take advantages of model gradient compression or model distillation.

Federated Learning

Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling On-the-Fly for Moving Object Detection

no code implementations • 14 Jun 2020 • Zerui Shao, Yi-Fei PU, Jiliu Zhou, Bihan Wen, Yi Zhang

Robust Principal Component Analysis (RPCA), as one of the most popular moving object modelling methods, aims to separate the temporally varying (i. e., moving) foreground objects from the static background in video, assuming the background frames to be low-rank while the foreground to be spatially sparse.

Moving Object Detection Object +2