基于学习博弈和契约论的分层联邦学习隐私保护激励机制

分层联邦学习(hierarchical federated learning, HFL)旨在通过多层架构的协作学习, 同时保护隐私和优化模型性能. 但其效果需依赖于针对参与各方的有效激励机制及应对信息不对称的策略. 为了解决上述问题, 本文提出一种保护终端设备、边缘服务器及云服务器隐私的分层激励机制. 在边端层, 边缘服务器作为中介应用多维合约理论设计不同类型的契约项, 促使终端设备在不泄露数据采集、模型训练以及模型传输成本的情况下, 使用本地数据参与HFL. 在云边层, 云服务器与边缘服务器间关于单位数据奖励和数据量的关系通过Stackelberg博弈进行建模, 在不泄露边缘服务器单位利润的情况下, 进一步将其转化为马尔可夫过程, 并采用保护隐私的多智能体深度强化学习(multi-agent deep reinforcement learning, MADRL)方法逐渐接近斯塔克伯格均衡(Stackelberg equilibrium, SE). 实验结果表明, 本文提出的分层激励机制在性能上优于基线方法, 云服务器的收益提升了接近11%, 单位成本获取增益提升接近18倍.

Privacy-preserving Incentive Mechanism for Hierarchical Federated Learning Combining Learning Game and Contract Theory

Hierarchical federated learning (HFL) aims to optimize model performance and maintain data privacy through multi-layered collaborative learning. However, its effectiveness relies on effective incentive mechanisms for participating parties and strategies to address information asymmetry. To address these issues, this study proposes a layered incentive mechanism for protecting the privacy of end devices, edge servers, and cloud servers. At the edge-device layer, edge servers act as intermediaries, using the multi-dimensional contract theory to design a variety of contract items. This encourages end devices to participate in HFL using local data without disclosing the costs of data collection, model training, and model transmission. At the cloud-edge layer, the Stackelberg game models the relationship between unit data reward and data size between a cloud server and edge servers and subsequently transforms it into a Markov process, all while maintaining the confidentiality of the edge servers’ unit profit. Then, multi-agent deep reinforcement learning (MADRL) is used to incrementally approach the Stackelberg equilibrium (SE) while ensuring privacy. Experimental results indicate that the proposed incentive mechanism outperforms traditional approaches, yielding an almost 11% increase in cloud server revenue and an approximately 18 times improvement in the cost-effectiveness gained.