关于联邦推系统的调查
IEEE transactions on neural networks and learning systems
|February 7, 2024
概括
联合推系统 (FedRSs) 通过使用中间参数而不是原始数据培训模型来增强用户隐私. 这项调查回顾了FedRS的隐私,安全和效率等挑战,指导未来的研究.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 信息安全 信息安全
背景情况:
- 联合学习 (FL) 在推系统中越来越多地用于保护用户隐私.
- FL允许使用聚合参数进行模型训练,绕过直接访问敏感用户数据.
- 联邦推系统 (FedRSs) 在遵守隐私法规的同时提供协作改进.
研究的目的:
- 为联邦推系统 (FedRSs) 提供当前研究环境的全面调查.
- 识别和分析包括隐私,安全,异质性和通信成本在内的关键挑战.
- 通过总结进展情况和强调未来的研究方向,指导研究人员和从业人员.
主要方法:
- 文献综述和对FedRSs现有研究的综合.
- 隐私机制,安全攻击和防御的分类和分析.
- 总结了解决系统异质性和通信效率问题的方法.
主要成果:
- 详细概述FedRS中常见的隐私保护技术,以及它们各自的优缺点.
- 审查新出现的安全威胁和相应的防御战略.
- 编制方法以减轻异质性和减少通信开支.
- 介绍实用的FedRS应用程序和基准数据集.
结论:
- 联邦储备储备系统 (FedRS) 对保护隐私的建议提出了有希望的方法,但面临着重大的技术障碍.
- 解决隐私,安全,异质性和通信效率对于广泛采用至关重要.
- 本调查确定了关键的研究缺口,并概述了未来FedRSs进步的有希望的途径.
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