计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 33-39.doi: 10.11896/jsjkx.220300031
瞿祥谋, 吴映波, 蒋晓玲
QU Xiang-mou, WU Ying-bo, JIANG Xiao-ling
摘要: 在联邦学习中,由于用户的本地数据分布会随着用户所在地以及用户偏好而变动,数据的非独立同分布下的用户数据可能缺少某些标签类别的数据,在模型聚合中显著影响了迭代更新速率和最终的模型性能。为了解决这一问题,提出了一种基于条件生成对抗网络进行联邦数据增强的算法,能够在不涉及泄露用户隐私的前提下,通过生成对抗网络模型对数据偏斜的参与者扩增少量数据,大幅提升非独立同分布数据划分下联邦学习算法的性能。实验结果表明,与当前主流的联邦算法相比,该算法在非独立同分布设置下的MNIST,CIFAR-10数据集上的预测精度分别提升了1.18%和14.6%,显示出了该算法对非独立同分布问题的有效性和实用性。
中图分类号:
[1]MCMAHAN H B,MOORE E,RAMAGE D,et al.Communication-efficient learning of deep networks from decentralized data[C]//Artificial Intelligence and Statistics.PMLR,2017:1273-1282. [2]MCMAHAN H B,MOORE E,RAMAGE D,et al.Federatedlearning of deep networks using model averaging[J].arXiv:1602.05629,2016. [3]YANG Q,LIU Y,CHEN T,et al.Federated machine learning:Concept and applications[J].ACM Transactions on Intelligent Systems and Technology,2019,10(2):1-19. [4]JAKUB K,MCMAHAN H B,YU F X,et al.Federated lear-ning:Strategies for improving communication efficiency[J].ar-Xiv:1610.05492,2016. [5]JAKUB K,MCMAHAN H B,DANIEL R,et al.Federated Optimization:Distributed Machine Learning for On-Device Intelligence[J].arXiv:1610.02527,2016. [6]ZHAO Y,LI M,SUDA N,et al.Federated learning with non-iid data[J].arXiv:1806.00582,2018. [7]BONAWITZ K,EICHNER H,GRIESKAMP W,et al.Towards federated learning at scale:System design[C]//Proceedings of Machine Learning and Systems.2019,1:374-388. [8]LARIMIREDDY S P,KALE S,MOHRI M,et al.SCAFFOLD:Stochastic Controlled Averaging for On-Device Federated Lear-ning[C]//Proceedings of the International Conference on Machine Learning.PMLR,2020,119:5132-5143. [9]LI X,HUANG K,YANG W,et al.On the convergence of fedavg on non-iid data[J] arXiv:1907.02189,2019. [10]HSU T M H,QI H,BROWN M.Measuring the effects of non-identical data distribution for federated visual classification[J].arXiv:1909.06335,2019. [11]LI T,SAHU A K,ZAHEER M,et al.Federated optimization in heterogeneous networks[C]//Proceedings of Machine Learning and Systems.2020:429-450. [12]WANG J,LIU Q,LIANG H,et al.Tackling the objective inconsistency problem in heterogeneous federated optimization[C]//Advances in Neural Information Processing Systems.2020:7611-7623. [13]KAIROUZ P,MCMAHAN H B,AVENT B,et al.Advances and openproblems in federated learning[J].Foundations and Trends in Machine Learning,2021,14(1/2):1-210. [14]SATTLER F,WIREDEMANN S,MULLER KR,et al.Robust and communication-efficient federated learning from non-iid data[J].IEEE Transactions on Neural Networks and Learning Systems,2019,31(9):3400-3413. [15]NISHIO T,YONETANI R.Client selection for federated lear-ning with heterogeneous resources in mobile edge[C]//International Conference on Communications(ICC).IEEE,2019:1-7. [16]WANG L,WANG W,LI B.CMFL:Mitigating communication overhead for federated learning[C]//International Conference on Distributed Computing Systems(ICDCS).IEEE,2019:954-964. [17]SMITH V,CHIANG C K,SANJABI M,et al.Federated multi-task learning[C]//Advances in Neural Information Processing Systems.2017. [18]SATTLER F,MULLER K R,SAMEK W.Clustered federated learning:Model-agnostic distributed multitask optimization under privacy constraints[J].IEEE Transactions on Neural Networks and Learning Systems,2020,32(8):3710-3722. [19]LI R,MA F,JIANG W,et al.Online federated multitask lear-ning[C]//International Conference on Big Data(Big Data).IEEE,2019:215-220. [20]COLLINS L,HASSANI H,MOKHTARI A,et al.Exploiting shared representations for personalized federated learning[C]//International Conference on Machine Learning.PMLR,2021:2089-2099. [21]PAN S J,YANG Q.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering,2009,22(10):1345-1359. [22]YANG H,HE H,ZHANG W,et al.FedSteg:A federated transfer learning framework for secure image steganalysis[J].IEEE Transactions on Network Science and Engineering,2020,8(2):1084-1094. [23]LIU Y,KANG Y,XING C,et al.A secure federated transfer learning framework[J].IEEE Intelligent Systems,2020,35(4):70-82. [24]XU M,LI X,WANG Y,et al.Privacy-preserving multisourcetransfer learning in intrusion detection system[J].Transactions on Emerging Telecommunications Technologies,2021,32(5):e3957. [25]JING Q,WANG W,ZHANG J,et al.Quantifying the perfor-mance of federated transfer learning[J].arXiv:1912.12795,2019. [26]SHARMA S,XING C,LIU Y.Secure and efficient federated transfer learning[C]//International Conference on Big Data(Big Data).IEEE,2019:2569-2576. [27]WANG Z,SONG M,ZHANG Z,et al.Beyond inferring class representatives:User-level privacy leakage from federated lear-ning[C]//IEEE Conference on Computer Communications.IEEE,2019:2512-2520. [28]SUN J,LI A,WANG B,et al.Soteria:Provable defense against privacy leakage in federated learning from representation perspective[C]//IEEE Conference on Computer Vision and Pattern Recognition.2021:9311-9319. [29]GOODFELLOW I,POUGET-ABADIE J,MIRZA MEHDI,et al.Generative adversarial nets[C]//Advances in Neural Information Processing Systems.2014. [30]DWORK C.Differential privacy:A survey of results[C]//International Conference on Theory and Applications of Models of Computation.Berlin:Springer,2008:1-19. [31]LIU J,YIN S,LI H,et al.A Density-based Clustering Method for K-anonymity Privacy Protection[J].Journal of Information Hiding and Multimedia Signal Processing,2017,8(1):12-18. [32]YANG Z,CHEN M,SAAD W,et al.Energy efficient federated learning over wireless communication networks[J].IEEE Transactions on Wireless Communications,2020,20(3):1935-1949. [33]HAMER J,MOHRI M,SURESH A T.Fedboost:A communication-efficient algorithm for federated learning[C]//International Conference on Machine Learning.PMLR,2020:3973-3983. [34]WAHAB O A,MOURAD A,OTROK H,et al.Federated machine learning:Survey,multi-level classification,desirable criteria and future directions in communication and networking systems[J].IEEE Communications Surveys & Tutorials,2021,23(2):1342-1397. |
[1] | 张佳, 董守斌. 基于评论方面级用户偏好迁移的跨领域推荐算法 Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer 计算机科学, 2022, 49(9): 41-47. https://doi.org/10.11896/jsjkx.220200131 |
[2] | 鲁晨阳, 邓苏, 马武彬, 吴亚辉, 周浩浩. 基于分层抽样优化的面向异构客户端的联邦学习 Federated Learning Based on Stratified Sampling Optimization for Heterogeneous Clients 计算机科学, 2022, 49(9): 183-193. https://doi.org/10.11896/jsjkx.220500263 |
[3] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
[4] | 吕由, 吴文渊. 隐私保护线性回归方案与应用 Privacy-preserving Linear Regression Scheme and Its Application 计算机科学, 2022, 49(9): 318-325. https://doi.org/10.11896/jsjkx.220300190 |
[5] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
[6] | 戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105 |
[7] | 陈明鑫, 张钧波, 李天瑞. 联邦学习攻防研究综述 Survey on Attacks and Defenses in Federated Learning 计算机科学, 2022, 49(7): 310-323. https://doi.org/10.11896/jsjkx.211000079 |
[8] | 黄觉, 周春来. 基于本地化差分隐私的频率特征提取 Frequency Feature Extraction Based on Localized Differential Privacy 计算机科学, 2022, 49(7): 350-356. https://doi.org/10.11896/jsjkx.210900229 |
[9] | 鲁晨阳, 邓苏, 马武彬, 吴亚辉, 周浩浩. 基于DBSCAN聚类的集群联邦学习方法 Clustered Federated Learning Methods Based on DBSCAN Clustering 计算机科学, 2022, 49(6A): 232-237. https://doi.org/10.11896/jsjkx.211100059 |
[10] | 王建明, 陈响育, 杨自忠, 史晨阳, 张宇航, 钱正坤. 不同数据增强方法对模型识别精度的影响 Influence of Different Data Augmentation Methods on Model Recognition Accuracy 计算机科学, 2022, 49(6A): 418-423. https://doi.org/10.11896/jsjkx.210700210 |
[11] | 闫萌, 林英, 聂志深, 曹一凡, 皮欢, 张兰. 一种提高联邦学习模型鲁棒性的训练方法 Training Method to Improve Robustness of Federated Learning 计算机科学, 2022, 49(6A): 496-501. https://doi.org/10.11896/jsjkx.210400298 |
[12] | 王健. 基于隐私保护的反向传播神经网络学习算法 Back-propagation Neural Network Learning Algorithm Based on Privacy Preserving 计算机科学, 2022, 49(6A): 575-580. https://doi.org/10.11896/jsjkx.211100155 |
[13] | 蔡欣雨, 冯翔, 虞慧群. 自适应权重的级联增强节点的宽度学习算法 Adaptive Weight Based Broad Learning Algorithm for Cascaded Enhanced Nodes 计算机科学, 2022, 49(6): 134-141. https://doi.org/10.11896/jsjkx.210500119 |
[14] | 尹文兵, 高戈, 曾邦, 王霄, 陈怡. 基于时频域生成对抗网络的语音增强算法 Speech Enhancement Based on Time-Frequency Domain GAN 计算机科学, 2022, 49(6): 187-192. https://doi.org/10.11896/jsjkx.210500114 |
[15] | 徐辉, 康金梦, 张加万. 基于特征感知的数字壁画复原方法 Digital Mural Inpainting Method Based on Feature Perception 计算机科学, 2022, 49(6): 217-223. https://doi.org/10.11896/jsjkx.210500105 |
|