2 papers
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.NI2025
Optimizing Split Federated Learning with Unstable Client Participation
Wei Wei, Zheng Lin, Xihui Liu +3
To enable training of large artificial intelligence (AI) models at the network edge, split federated learning (SFL) has emerged as a promising approach by distributing computation…