FedCoSR:非IID数据中的标签异质性与对比可共享表示的个性化联合学习
IEEE transactions on cybernetics
|September 3, 2025
概括
这项研究介绍了联合对比可共享表示 (FedCoSRs),这是一个新的隐私保护联合学习算法. 通过解决标签分配偏差和数据稀缺问题,FedCoSR提高了分布式计算应用的准确性和公平性.
科学领域:
- 人工智能
- 机器学习
- 分布式计算
背景情况:
- 分布式计算应用中的标签分配偏差和数据稀缺导致不准确和不公平.
- 现有的联合学习方法很难有效地应对这些异质性挑战.
研究的目的:
- 提出一个新的联合学习算法,联合对比可共享表示 (FedCoSRs),以减轻IC应用中的不准确性和不公平性.
- 在分布式环境中保持数据隐私的同时,促进客户之间的知识共享.
主要方法:
- 在全球范围内,FedCoSR汇总了当地模型的浅层参数和典型的当地表示.
- 在本地和全球代表之间使用对比学习来丰富本地知识,并对抗标签偏差导致的业绩下降.
- 通过协调全球模型参与,为数据稀缺的客户提供公平性.
主要成果:
- 模拟显示FedCoSRs有效地减轻了标签异质性.
- 与现有方法相比,拟议的算法在准确性和公平性方面取得了显著的改进.
- 在不同程度的标签异质性数据集中,FedCoSRs表现出有效性.
结论:
- 在异质数据条件下,FedCoSR提供了一种可靠的解决方案,以提高联合学习的准确性和公平性.
- 在分布式计算环境中,该算法成功地平衡了知识共享与数据隐私.
- FedCoSRs在解决联合学习应用中的数据异质性挑战方面取得了重大进展.
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