1 code implementation • 23 Feb 2024 • Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han
These hard samples are then employed to promote the quality of the ensemble model by adjusting the ensembling weights for each client model.
1 code implementation • 1 Jun 2022 • Rong Dai, Li Shen, Fengxiang He, Xinmei Tian, DaCheng Tao
In this work, we propose a novel personalized federated learning framework in a decentralized (peer-to-peer) communication protocol named Dis-PFL, which employs personalized sparse masks to customize sparse local models on the edge.