FedEmerge:用于传感器网络和边缘情报的透导向联合学习方法
1Department of Computing and Information Technology, Faculty of Science and Technology, The University of the West Indies, St. Augustine Campus, St. Augustine 350462, Trinidad and Tobago.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
联合学习 (FL) 可以通过使用引导方法优先考虑各种客户端数据来改进. 在异质网络中,FedEmerge提高了全球模型的准确性和融合速度.
科学领域:
- 分布式机器学习 分布式机器学习
- 边缘情报 边缘情报是指边缘情报.
- 数据隐私 数据隐私
背景情况:
- 联合学习 (FL) 允许在不共享原始数据的情况下进行协作模型培训,这对于传感器网络的隐私和带宽至关重要.
- 像FedAvg这样的传统FL方法与非IID数据进行斗争,这在边缘环境中很常见.
- 现有的聚合方法可能会忽视数据的信息性,影响稀疏或不平衡的客户端数据集的性能.
研究的目的:
- 为联邦学习引入FedEmerge,一种以为导向的聚合方法.
- 通过优先考虑基于数据多样性和信息的客户端更新来提高全球模型性能.
- 实现新兴的集体学习动态,以改善异质网络中的融合.
主要方法:
- 开发了FedEmerge,这是FL的导聚合策略.
- 使用信息来调整更新权重的量化客户端数据多样性.
- 在Polyak-Łojasiewicz (PL) 条件下证明了FedEmerge的趋同.
主要成果:
- FedEmerge实现了线性收,相当于集中式梯度下降.
- 经验结果显示,在扭曲的非IID基准指标上,准确度提高,收速度更快.
- 与FedAvg相比,在CIFAR-10,联邦EMNIST和莎士比亚数据集中减少了客户之间的绩效差异.
结论:
- 以透为指导的聚合通过根据数据多样性对客户端更新进行加权来提高异质网络中的FL结果.
- 对于现实世界的联合系统,FedEmerge提供了一个实用的,保护隐私的解决方案,计算开销最小.
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