Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts
Yijun Lu, Zihan Fang, Pengpeng Qiao +6
The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via…
cs.LG2026
SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression
Zehang Lin, Miao Yang, Haihan Zhu +9
The growing complexity of neural networks hinders the deployment of distributed machine learning on resource-constrained devices. Split learning (SL) offers a promising solution by…