通过安全的人工智能提高工作场所的生产力,使用联合的对比学习模型来优化绩效
1Faculty of Management, SRM Institute of Science and Technology, Kattankulathur, Chennai, India. gm@srmist.edu.in.
Scientific reports
|November 21, 2025
概括
本研究引入了联合对比学习 (FCL) 框架,以增强工作场所生产力分析. 在分散的环境中,FCL模型显著提高了AI准确性和数据隐私,超过了传统方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 集中式人工智能数据处理对员工隐私构成风险,不适合分散式企业环境.
- 现有的人工智能模型与可扩展性,数据安全性以及集中学习中固有的偏见作斗争.
- 工作场所的生产力分析至关重要,但受到数据处理中的隐私和效率担忧的阻碍.
研究的目的:
- 开发一个保护隐私的人工智能模型,用于分散的工作场所生产力分析.
- 在联合学习中增强AI模型预测准确性,稳定性和通信效率.
- 通过提出安全和可扩展的联合学习框架来解决集中数据处理的局限性.
主要方法:
- 联邦对比学习 (FCL) 框架集成了对比学习,联邦平均和同态加密.
- 在分区的去中心化节点上进行的实验分析模拟了现实世界的联合学习场景.
- 利用员工绩效和生产力数据集进行模型培训和评估.
主要成果:
- 拟议的FCL模型实现了98.9%的全球准确性,超过了FedAvg (91.4%),LSTM (87.6%) 和CNN (81.2%).
- 在精度 (98.5%),回忆 (97.8%) 和F1得分 (97.9%) 方面表现出高性能.
- 显著减少了97.2%的数据泄漏,并提高了95.2%的梯度压缩效率,降低了通讯开销.
结论:
- FCL框架为工作场所生产力分析中的联合学习提供了一种高效,可扩展和保护隐私的解决方案.
- 这种方法可以通过超越集中式数据环境,实现工作场所的智能转型.
- 该研究强调了FCL在未来工作中确保和适应AI系统的潜力.
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