Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 232-237.doi: 10.11896/jsjkx.211100059

• Big Data & Data Science • Previous Articles     Next Articles

Clustered Federated Learning Methods Based on DBSCAN Clustering

LU Chen-yang, DENG Su, MA Wu-bin, WU Ya-hui, ZHOU Hao-hao   

  1. Science and Technology on Information Systems Engineering Laboratory,National University of Defense Technology,Changsha 410073,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:LU Chen-yang,born in 1997,postgra-duate.His main research interests include federated learning and machine lear-ning.
    MA Wu-bin,born in 1986,Ph.D,lectu-rer.His main research interests include data engineering and cyber-physical systems.
  • Supported by:
    General Program of National Natural Science Foundation of China(61871388).

Abstract: Federated learning is to solve the problem of data fragmentation and isolation in machine learning based on privacy protection.Each client node trains the data locally and uploads the model parameter information to the central server,which aggregates the parameter information to achieve the purpose of common training.In the real environment,the distribution of data among nodes is often inconsistent.By analyzing the influence of independent identically distributed data on the accuracy of federated learning,it is proved that the accuracy of the model obtained by the traditional federated learning method is low.Therefore,a diversified sampling strategy is adopted to simulate the data inclination distribution,and a Clustered Federated Learning Methods algorithm based on DBSCAN clustering(DCFL) is proposed,which solves the problem that the learning accuracy is reduced when the data of different nodes are not independently and identically distributed in federated learning.Through the experimental comparison of Mnist and Cifar-10 standard data sets,compared with the traditional federated learning algorithm,DCFL can greatly improve the accuracy of the model.

Key words: Client selection, Cluster, Data distribution, Federated learning, Training optimization

CLC Number: 

  • TP301
[1] SHULTZ D.When your voice betrays you[J].Science,2015,347(6221):494-494.
[2] YANG Q,LIU Y,CHEN T,et al.Federated Machine Learning[J].ACM Transactions on Intelligent Systems and Technology,2019,10(2):1-19.
[3] BONAWITZ K.Practical Secure Aggregation for Privacy-Pre-serving Machine Learning[C]//Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security.2017.
[4] AI and Data Privacy Protection:The Way to Federated Learning[J].Journal of Information Security Research,2019,5(11):961-965.
[5] SATTLER F,WIEDEMANN S,MULLER K R,et al.Robustand Communication-Efficient Federated Learning From Non-i.i.d.Data[J].IEEE Trans Neural Netw Learn Syst,2020,31(9):3400-3413.
[6] LI T,SAHU A K,TALWALKAR A,et al.Federated Learning:Challenges,Methods,and Future Directions[J].IEEE Signal Processing Magazine,2020,37(3):50-60.
[7] MCMAHAN H B,MOORE E,D RAMAGE,et al.Communication-Efficient Learning of Deep Networks from Decentralized Data[J].arXiv:1602.05629,2016.
[8] KAIROUZ P,BRENDAN H.McMahan.Advances and OpenProblems in Federated Learning[J].arXiv:1912.04977,2021.
[9] LI X,HUANG K,YANG W,et al.On the Convergence of Fed-Avg on Non-IID Data[J].arXiv:1907.02189,2019.
[10] ZHAO Y,LI M,LAI L,et al.Federated Learning with Non-IIDData[J].arXiv:1806.00582,2018.
[11] JIANG Y,KONEN J,RUSH K,et al.Improving FederatedLearning Personalization via Model Agnostic Meta Learning[J].arXiv:1909.12488,2019.
[12] MUHAMMAD K.FedFast:Going Beyond Average for FasterTraining of Federated Recommender Systems[C]//Proceedings of the 26th ACM SIGKDD International Conference on Know-ledge Discovery & Data Mining.2020.
[13] GHOSH A,HONG J,YIN D,et al.Robust Federated Learning in a Heterogeneous Environment[J].arXiv:1906.06629,2019.
[14] SATTLER F,MULLER K R,SAMEK W.Clustered Federated Learning:Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints[J].IEEE Transactions on Neural Network Learning and Systems,2021,32(8):3710-3722.
[15] ESTER M,KRIEGEL H P,SANDER J,et al.A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]//AAAI Press.1996.
[16] KONEN J,MCMAHAN H B,YU F X,et al.Federated Lear-ning:Strategies for Improving Communication Efficiency[J].ar-Xiv:1610.05492 2016.
[17] BONAWITZ K,EICHNER H,GRIESKAMP W,et al.Towards Federated Learning at Scale:System Design[J].arXiv:1902.01046,2019.
[18] MCMAHAN H B,RAMAGE D,TALWAR K,et al.LearningDifferentially Private Recurrent Language Models[J].arXiv:1710.06963,2017.
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