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cs.LG2025
Towards Instance-wise Personalized Federated Learning via Semi-Implicit Bayesian Prompt Tuning
Tiandi Ye, Wenyan Liu, Kai Yao +6
Federated learning (FL) is a privacy-preserving machine learning paradigm that enables collaborative model training across multiple distributed clients without disclosing their raw…
cs.LG2025
Data Assetization via Resources-decoupled Federated Learning
Jianzhe Zhao, Feida Zhu, Lingyan He +4
With the development of the digital economy, data is increasingly recognized as an essential resource for both work and life. However, due to privacy concerns, data owners tend to…
cs.LG2023★ 1 cited
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence
Shu Zheng, Tiandi Ye, Xiang Li +1
Federated learning (FL) on heterogeneous data (non-IID data) has recently received great attention. Most existing methods focus on studying the convergence guarantees for the globa…