针对多样化的全球模型进行定制的联合学习,具有适应性中央加速
IEEE transactions on neural networks and learning systems
|November 5, 2024
概括
本研究引入了新的联合学习 (FL) 方法,MA-FSVRG和GA-FSVRG,以改善具有不同需求的机器之间的协作. 这些方法可以实现定制解决方案,同时最大限度地提高单个机器的利.
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:
- 联邦式学习 (FL) 面临的挑战是为了个人机器的利益而聚合不同的本地模型.
- 一个单一的全球模型可能与每个机器的独特利益和利最大化目标不一致.
研究的目的:
- 开发加速联合培训程序,使机器能够利用共享知识,同时实现定制解决方案.
- 通过提出新的分组和加速机制来解决FL的异质需求.
主要方法:
- 引入了基于模型的分组机制与自适应中心加速 (MA-FSVRG) 基于联邦随机差异减小梯度 (FSVRG) 框架.
- 在FSVRG框架内,拟议的基于梯度的集群机制与自适应中心加速 (GA-FSVRG).
- 通过模拟对最先进的FL基线进行性能评估.
主要成果:
- 无论是MA-FSVRG还是GA-FSVRG,都显示出与现有的FL方法相比的优势.
- MA-FSVRG提供了增强的性能稳定性和降低了本地计算成本.
- GA-FSVRG实现了卓越的测试准确性和更快的融合,特别是在有限的机器参与的情况下.
结论:
- 拟议的MA-FSVRG和GA-FSVRG有效地解决了联合学习中的异质需求.
- 这些方法可以实现个性化的模型优化,并在FL设置中提高协作效率.
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