联合学习中通信成本的局限性和未来方面:一项调查
Muhammad Asad1, Saima Shaukat1, Dou Hu1
1Graduate School of Information Science and Technology, Department of Creative Informatics, The University of Tokyo, Tokyo 113-8654, Japan.
本文回顾了分布式系统的通信效率高的联合学习 (FL) 方法. 它涵盖了模型压缩和客户端选择等技术,以优化FL性能,并解决未来的研究挑战.
科学领域:
- 分布式系统 分布式系统
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 联合学习 (FL) 可以在不集中数据的情况下,在多个客户端之间进行分布式模型训练.
- 现代分布式系统需要高效的沟通策略,以便有效地实施FL.
研究的目的:
- 调查和分析沟通效率高的联合学习方法.
- 识别FL的优化技术和当前挑战.
主要方法:
- 审查FL的沟通策略,包括模型更新和压缩.
- 在边缘/云环境中分析资源管理和客户选择.
- 检查优化技术,如压缩方案和结构化更新.
主要成果:
- 确定了各种提高FL通信效率的技术.
- 强调了压缩,资源管理和客户端选择的重要性.
- 概述了当前的研究挑战和未来的研究方向.
结论:
- 通信效率对于分布式系统中联合学习的成功至关重要.
- 需要进一步的研究来应对现有的挑战,并探索未来的FL进步.
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