基于深度强化学习的非IID联合学习的优化方法
Xutao Meng1, Yong Li1,2,3, Jianchao Lu4
1School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
联邦学习 (FL) 在非IID数据方面扎. 我们的FedRLCS框架使用深度强化学习来选择最佳客户,加快融合并减少10-70%的沟通轮回,以获得更好的模型培训.
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) 允许在没有数据共享的情况下进行协作模式培训.
- 客户之间非独立且相同分布的 (非IID) 数据阻碍了FL的融合速度和准确性.
- 现有的FL方法在有效处理非IID数据分布方面面临挑战.
研究的目的:
- 开发一个新的联合学习框架,FedRLCS,以应对非IID数据的挑战.
- 为了加快模型的融合,并提高 FL 设置的准确性与异质数据.
- 优化客户选择,以实现高效的协作培训.
主要方法:
- 设计了FedRLCS,这是一个集成深度强化学习 (DRL) 的联合学习框架.
- 增强了双DQN (DDQN) 算法的贪策略和行动空间,以实现最佳的客户端子集选择.
- 使用分区数据集对客户端进行模拟的非IID数据分布.
主要成果:
- 与最先进的非IID FL方法相比,FedRLCS显著减少了10-70%的通信轮.
- 该框架在各种数据集和模型中实现了更少的通信时代的目标准确性.
- 客户不需要额外的计算或存储成本.
结论:
- 通过非IID数据,FedRLCS有效地加速了融合,并提高了非IID数据的联合学习的表现.
- 基于DRL的客户选择是克服FL数据异质性的可行策略.
- 拟议的方法为面对数据孤岛的实际FL部署提供了有效的解决方案.
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