双面罩:通过直角梯度扰动和RL优化的PSO来实现隐私-实用性-效率三重问题的联合优化
Weibai Zhou1, Changlong Li1, Rong Li1
1School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou, Guangdong, China.
PloS one
|December 31, 2025
概括
双面膜通过动态平衡隐私,实用性和通信来增强联合学习. 这种新的框架提高了准确性和融合速度,同时降低了隐私预算和通信成本.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 联合学习面临着隐私-实用性-沟通三重难题.
- 现有的静态防御缺乏动态适应和协调.
- 在动态平衡这些权衡方面存在关键差距.
研究的目的:
- 提出DualMask,一个合作优化框架,用于在联合学习中实现动态三难题平衡.
- 解决联合学习中静态防御机制的局限性.
- 改善隐私-实用性-通信权衡的边界.
主要方法:
- 客户端自适应直角噪声取消器 (AONC) 具有层级自适应剪切,进度感知噪声衰减和定向调整.
- 服务器端分布式决斗双深Q网络 (D3QN) 用于动态资源分配.
- 基于粒子集群优化 (PSO) 的聚合用于非IID偏差校正.
主要成果:
- 在非IID设置中,DualMask实现了5.2%的更高精度 (84.1%与79.4%相比).
- 收速度增加了34.4% (210轮与320轮相比).
- 隐私预算从4.5降低到2.8,通信成本降低了37.2% (45MB对65MB).
结论:
- 双面膜在联合学习中提供了显著的帕雷托改进.
- 该框架有效地扩大了隐私-实用性-通讯三难题的边界.
- 在准确性,融合性,隐私性和通信效率方面表现优于FedAvg.
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