FedLSC:提高与落后者和对手的联合学习中的沟通效率和稳定性
IEEE transactions on neural networks and learning systems
|August 18, 2025
概括
本研究介绍了FedLSC,这是一个联合学习 (FL) 框架,可以在没有公开数据的情况下提高效率和稳定性. 联邦电信通信系统 (FedLSC) 显著降低了通信成本,使得FL在现实应用中变得更加实用.
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) 面临着落后者,对手和高通讯成本等挑战.
- 现有的FL方法往往需要公开数据,这限制了现实世界的适用性和弹性.
研究的目的:
- 提出FedLSC,一个新的FL框架,旨在提高稳定性和效率.
- 通过消除在培训期间依赖公共数据来解决当前FL方法的局限性.
主要方法:
- 为了提高稳定性和效率,FedLSC使用层选择相关性 (LSC).
- 关键的创新包括层选择 (LS) 以减少通信,基于LS的缩放标志-随机梯度下降 (SSS) 以进行本地更新,以及基于LSC的聚合.
- 该SSS方案减轻了量子化损失和通信开销.
主要成果:
- 联邦电信通信系统 (FedLSC) 显著降低了通信成本,实现了最先进方法的0.01%.
- 该框架保持了性能,同时大幅减少了通信需求.
- 评估表明,在带宽受限制的FL场景中,性能和效率强.
结论:
- 对于现代联合学习应用程序,FedLSC提供了一种实用且有弹性的解决方案.
- 该框架有效地提高了FL系统的效率和稳定性.
- 在通信带宽有限的环境中,FedLSC特别有利.
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