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cs.LG2024
Decentralized Directed Collaboration for Personalized Federated Learning
Yingqi Liu, Yifan Shi, Qinglun Li +3
Personalized Federated Learning (PFL) is proposed to find the greatest personalized models for each client. To avoid the central failure and communication bottleneck in the server-…
cs.LG2024
AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained Optimization
Longxiang He, Li Shen, Xueqian Wang
Implicit Q-learning (IQL) serves as a strong baseline for offline RL, which learns the value function using only dataset actions through quantile regression. However, it is unclear…
cs.CV2024
Heterogeneous Federated Learning with Splited Language Model
Yifan Shi, Yuhui Zhang, Ziyue Huang +4
Federated Split Learning (FSL) is a promising distributed learning paradigm in practice, which gathers the strengths of both Federated Learning (FL) and Split Learning (SL) paradig…