3 papers
cs.LG2026
GRIP: Algorithm-Agnostic Machine Unlearning for Mixture-of-Experts via Geometric Router Constraints
Andy Zhu, Rongzhe Wei, Yupu Gu +1
Machine unlearning (MU) for large language models has become critical for AI safety, yet existing methods fail to generalize to Mixture-of-Experts (MoE) architectures. We identify…
cs.NI2026
SplitCom: Communication-efficient Split Federated Fine-tuning of LLMs via Temporal Compression
Tao Li, Yulin Tang, Yiyang Song +4
Federated fine-tuning of on-device large language models (LLMs) mitigates privacy concerns by preventing raw data sharing. However, the intensive computational and memory demands p…
cs.LG2026
MoEEdit: Efficient and Routing-Stable Knowledge Editing for Mixture-of-Experts LLMs
Yupu Gu, Rongzhe Wei, Andy Zhu +1
Knowledge editing (KE) enables precise modifications to factual content in large language models (LLMs). Existing KE methods are largely designed for dense architectures, limiting…