3 papers
cs.LG2026
Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation
Hong Chen, Pengcheng Wu, Yuanguo Lin +4
We rethink Federated Learning (FL) from a nested learning perspective, framing the core challenge as how to collaboratively learn optimization rules, not just static models, to tac…
cs.LG2025
Sharpness-aware Federated Graph Learning
Ruiyu Li, Peige Zhao, Guangxia Li +3
One of many impediments to applying graph neural networks (GNNs) to large-scale real-world graph data is the challenge of centralized training, which requires aggregating data from…
cs.CR2025
A Survey on Privacy Risks and Protection in Large Language Models
Kang Chen, Xiuze Zhou, Yuanguo Lin +3
Although Large Language Models (LLMs) have become increasingly integral to diverse applications, their capabilities raise significant privacy concerns. This survey offers a compreh…