面向非独立同分布数据的自适应联邦深度学习算法

Adaptive Federated Deep Learning With Non-IID Data

近些年,联邦学习(Federated learning,FL)由于能够打破数据壁垒,实现孤岛数据价值变现,受到了工业界和学术界的广泛关注.然而,在实际工程应用中,联邦学习存在着数据隐私泄露和模型性能损失的问题.为此,首先对这两个问题进行数学描述与分析.然后,提出一种自适应模型聚合方案,该方案能够设定各参与者的Mini-batch值和自适应调整全局模型聚合间隔,旨在保证模型精度的同时,提高联邦学习训练效率.并且,混沌系统被首次引入联邦学习领域中,用于构建一种基于混沌系统和同态加密的混合隐私保护方案,从而进一步提升系统的隐私保护水平.理论分析与实验结果表明,提出的联邦学习算法能够保证参与者的数据隐私安全.并且,在非独立同分布数据的场景下,该算法能够在保证模型精度的前提下提高训练效率,降低系统通信成本,具备实际工业场景应用的可行性.

In recent years,federated learning(FL)that can break data barriers and realize the value of isolated data,has been received wide-spread attention from industry and academia.However,in real industry applications,federated learning has problems with privacy leakage and model accuracy loss,which is analyzed through mathematical demonstration in this study.To solve the issues,this paper proposes an adaptive global model aggregation scheme that can adaptively set the Mini-batch value of each participant and the global model aggregation interval for the parameter server,which aims to improve the training efficiency while ensuring the accuracy of the model.Moreover,this paper introduces the chaos system into the federated learning field,which is used to construct a hybrid privacy-preserving scheme based on chaos system and homomorphic encryption,thereby further improving the privacy protection level.Theoretical analysis and experimental results show that the proposed approach can guarantee the data privacy security of participants.Moreover,in the non-independent and identically distributed(Non-IID)data scenario,the proposed method can improve the training efficiency and reduce communication cost while ensuring the model accuracy,which is feasible for real industrial applications.