3 papers
cs.LG2026
Task-Distributionally Robust Data-Free Meta-Learning
Zixuan Hu, Yongxian Wei, Li Shen +4
Data-Free Meta-Learning (DFML) aims to enable efficient learning of unseen few-shot tasks, by meta-learning from multiple pre-trained models without accessing their original traini…
cs.LG2025
Reliable Imputed-Sample Assisted Vertical Federated Learning
Yaopei Zeng, Lei Liu, Shaoguo Liu +3
Vertical Federated Learning (VFL) is a well-known FL variant that enables multiple parties to collaboratively train a model without sharing their raw data. Existing VFL approaches…
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-…