联合学习:概述,战略,应用,工具和未来方向
Betul Yurdem1, Murat Kuzlu2, Mehmet Kemal Gullu1
1Department of Electrical and Electronics Engineering, Izmir Bakircay University, Izmir, Turkey.
Heliyon
|October 11, 2024
概括
联合学习 (FL) 允许在不共享原始数据的情况下进行协作模式培训,增强隐私和安全性. 这种方法为各种应用提供了可扩展的解决方案,解决了关键的保密问题.
科学领域:
- 计算机科学 计算机科学
- 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) 是一种分布式机器学习范式.
- 它可以在多个去中心化节点之间进行共享模型的协作训练.
- FL通过将原始数据本地化并仅共享模型更新来确保数据隐私.
研究的目的:
- 提供关于联合学习的全面审查.
- 涵盖其原则,战略,应用和工具.
- 确定机遇,挑战和未来的研究方向在FL.
主要方法:
- 关于联合学习原则和策略的文献综述.
- 分析现有的联合学习应用程序和工具.
- 讨论挑战和未来的研究途径.
主要成果:
- 联合学习显著提高数据隐私和安全性.
- 主要优势包括分布式环境中的可扩展性和效率.
- 在需要保密的高风险应用中,FL策略特别有利.
结论:
- 联合学习是一种强大的隐私保护机器学习方法.
- 它解决了敏感领域的关键数据保密问题.
- 需要进一步的研究来探索它的全部潜力,并克服现有的挑战.
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