优化客户参与通信受限制的联合LLM适应与LoRA客户参与
Faranaksadat Solat1, Joohyung Lee1
1Department of Computing, Gachon University, Seongnam 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
通过LoRaC-GA改进了大型语言模型的联合学习,LoRaC-GA是一个新的框架,优化了客户端选择以降低通信成本. 这种方法在带宽受限制的边缘环境中提高了效率.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 促进了大型语言模型 (LLM) 的隐私保护适应.
- 在FL的高通讯上空成本阻碍了在边缘环境中的部署.
- 参数效率微调 (PEFT),就像低级调整 (LoRA) 一样,减少了LLM更新大小.
研究的目的:
- 提出LoRaC-GA,这是一个具有LLMs的FL的沟通意识优化框架.
- 在带宽限制下,动态确定每轮客户端的最佳数量.
- 为了最大限度地提高模型准确性和通信效率.
主要方法:
- 制定了一个最大限度的目标,以实现共同的准确性和通信效率.
- 使用遗传算法 (GA) 来解决非凸的优化问题.
- 集成了一个结构化的点对点协作协议,具有log2K复杂度.
主要成果:
- LoRaC-GA可自适应地选择每个回合的最佳客户数.
- 该框架实现了具有竞争力的准确性,并大大降低了通信成本.
- 在带宽受限制的边缘部署中表现出有效性.
结论:
- 在边缘设置中,LoRaC-GA提高了大型LLM的FL可行性.
- 该框架为通信有限的环境提供了可扩展和高效的解决方案.
- 优化客户选择对于高效的联合LLM适应至关重要.
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