研究人员推出了一种自适应联邦联邦学习系统SplitFT，旨在克服分布式客户端微调大语言模型（LLMs）的挑战。该系统允许客户端动态设置其切分层以适应数据和设备的异构性，同时通过调整LoRA秩来减少通信开销。实验结果表明，SplitFT在各种基准测试中的微调效率和模型性能方面优于现有方法。此外，一篇综述论文系统地回顾和分类了联邦学习在LLM微调领域的最新进展，重点关注模型优化、系统效率和隐私保护。 AI
影响 使资源受限的组织能够更高效、更注重隐私地微调LLM。
排序理由 该集群包含两篇arXiv论文，一篇提出了LLM微调的新系统，另一篇对该领域进行了综述。
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arXiv:2604.26388v1 Announce Type: cross Abstract: Federated Split Learning has been identified as an efficient approach to address the computational resource constraints of clients in classical federated learning, while guaranteeing data privacy for distributed model training acr…
Federated Split Learning has been identified as an efficient approach to address the computational resource constraints of clients in classical federated learning, while guaranteeing data privacy for distributed model training across data owners. However, it faces some critical c…
Federated Split Learning has been identified as an efficient approach to address the computational resource constraints of clients in classical federated learning, while guaranteeing data privacy for distributed model training across data owners. However, it faces some critical c…
arXiv:2604.24468v1 Announce Type: cross Abstract: Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for resource-constrained organizations. While cloud platforms could provide the needed resou…
Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for resource-constrained organizations. While cloud platforms could provide the needed resources, data privacy concerns make sharing sensitive…