24 citations · 87 across the 18 of their papers we have counts for
4 papers · 1 filter
Meta Generative Flow Networks with Personalization for Task-Specific Adaptation
Xinyuan Ji, Xu Zhang, Wei Xi +3
Multi-task reinforcement learning and meta-reinforcement learning have been developed to quickly adapt to new tasks, but they tend to focus on tasks with higher rewards and more fr…
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
Xu Zhang, Wenpeng Li, Yunfeng Shao +3
Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients. However, FL suffers performance degradation from…
Personalized Federated Learning via Variational Bayesian Inference
Xu Zhang, Yinchuan Li, Wenpeng Li +2
Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients. To address these challenges, this paper proposes a…
Sparse Personalized Federated Learning
Xiaofeng Liu, Yinchuan Li, Qing Wang +3
Federated Learning (FL) is a collaborative machine learning technique to train a global model without obtaining clients' private data. The main challenges in FL are statistical div…