通过取消学习来解决联邦学习中不可靠的本地模式
Muhammad Ameen1, Riaz Ullah Khan2, Pengfei Wang3
1Yangzte Delta Region Institute, University of Electronic Science and Technology of China, Huzhou, Zhejiang Province, 313001, PR China; School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, PR China.
概括
联邦取消学习 (FUL) 现在解决了不良数据和其他负面影响. 新的本地模型改进 (LMR) 方法提高了全球模型的准确性和失学速度.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 系统面临的挑战是由于局部模型不可靠,维持全球模型可靠性.
- 现有的联合取消学习 (FUL) 方法主要针对不良数据,忽视其他负面影响来源,如对抗性攻击或通信约束.
研究的目的:
- 引入局部模型精制 (LMR),一种新的FUL方法,旨在减轻不良数据和其他因素的负面影响.
- 在联合学习系统中提高全球模型的可靠性和准确性.
主要方法:
- LMR根据影响源:不良数据或其他因素对不可靠的本地模型进行分类.
- 坏数据影响取消学习 (BDIU) 是一个客户端算法,使用梯度上升来减轻坏数据的影响.
- 其他影响失学 (OIU) 是一个服务器端算法,使用先前的全球模型参数重建本地模型.
主要成果:
- 最低限量药物有效地识别和减轻来自不同来源的负面影响.
- 对MNIST,FMNIST,CIFAR-10和CelebA数据集的评估显示了更高的准确性.
- 与现有方法相比,LMR实现了平均5倍的失学加速.
结论:
- 通过解决多个不可靠性来源,LMR提供了联合取消学习的综合解决方案.
- 拟议的方法显著提高了全球模型的性能和联合学习中的失学效率.
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