LayerCFL: an efficient federated learning with layer-wised clustering

Cybersecurity - Tập 6 Số 1 - Trang 1-14 - 2023
Yuan, Jie1,2, Qian, Rui1,2, Yuan, Tingting3, Sun, Mingliang1,2, Li, Jirui4, Li, Xiaoyong1,2
1School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China
2Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing, China
3Institute of Computer Science, Faculty of Mathematics and Computer Science, University of Goettingen, Göettingen, Germany
4School of Information Technology, Henan University of Chinese Medicine, Zhengzhou, China

Tóm tắt

Federated Learning (FL) suffers from the Non-IID problem in practice, which poses a challenge for efficient and accurate model training. To address this challenge, prior research has introduced clustered FL (CFL), which involves clustering clients and training them separately. Despite its potential benefits, CFL can be computationally and communicationally expensive when the data distribution is unknown beforehand. This is because CFL involves the entire neural networks of involved clients in computing the clusters during training, which can become increasingly time-consuming with large-sized models. To tackle this issue, this paper proposes an efficient CFL approach called LayerCFL that employs a Layer-wised clustering technique. In LayerCFL, clients are clustered based on a limited number of layers of neural networks that are pre-selected using statistical and experimental methods. Our experimental results demonstrate the effectiveness of LayerCFL in mitigating the impact of Non-IID data, improving the accuracy of clustering, and enhancing computational efficiency.

Tài liệu tham khảo

citation_journal_title=Big Data Res; citation_title=Efficient machine learning for big data: a review; citation_author=OY Al-Jarrah, PD Yoo, S Muhaidat, GK Karagiannidis, K Taha; citation_volume=2; citation_issue=3; citation_publication_date=2015; citation_pages=87-93; citation_doi=10.1016/j.bdr.2015.04.001; citation_id=CR1 Chandran P, Bhat R, Chakravarthi A, Chandar S (2021) Weight divergence driven divide-and-conquer approach for optimal federated learning from non-iid data. arXiv:2106.14503 Cohen G, Afshar S, Tapson J, Van Schaik A (2017) Emnist: Extending mnist to handwritten letters. In: 2017 international joint conference on neural networks (IJCNN), IEEE, pp 2921–2926 citation_journal_title=Inform Fusion; citation_title=Non-iid data and continual learning processes in federated learning: a long road ahead; citation_author=MF Criado, FE Casado, R Iglesias, CV Regueiro, S Barro; citation_volume=88; citation_publication_date=2022; citation_pages=263-280; citation_doi=10.1016/j.inffus.2022.07.024; citation_id=CR4 Dennis DK, Li T, Smith V (2021) Heterogeneity for the win: One-shot federated clustering. In: International conference on machine learning, PMLR, pp 2611–2620 Gao C, Wang X, He X, Li Y (2022) Graph neural networks for recommender system. In: WSDM ’22: The fifteenth ACM international conference on Web search and data mining citation_journal_title=Adv Neural Inf Process Syst; citation_title=An efficient framework for clustered federated learning; citation_author=A Ghosh, J Chung, D Yin, K Ramchandran; citation_volume=33; citation_publication_date=2020; citation_pages=19,586-19,597; citation_id=CR7 citation_journal_title=Mobile Networks Appl; citation_title=Adaptive clustered federated learning for heterogeneous data in edge computing; citation_author=B Gong, T Xing, Z Liu, J Wang, X Liu; citation_volume=27; citation_issue=4; citation_publication_date=2022; citation_pages=1520-1530; citation_doi=10.1007/s11036-022-01978-8; citation_id=CR8 citation_journal_title=Foundations and Trends® in Machine Learning; citation_title=Advances and open problems in federated learning; citation_author=P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, K Bonawitz, Z Charles, G Cormode, R Cummings; citation_volume=14; citation_issue=1–2; citation_publication_date=2021; citation_pages=1-210; citation_doi=10.1561/2200000083; citation_id=CR9 Kemp S (2022) Digital 2022: Global overview report, datareportal, 2022. https://datareportal.com/reports/digital-2022-global-overview-report Kim H, Kim Y, Park H (2021) Reducing model cost based on the weights of each layer for federated learning clustering. In: 2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN), IEEE, pp 405–408 Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. University of Toronto, Tech Rep, Computer Science Department, p 1 Li Q, Diao Y, Chen Q, He B (2022) Federated learning on non-iid data silos: An experimental study. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp 965–978, https://doi.org/10.1109/ICDE53745.2022.00077 citation_journal_title=IEEE Signal Process Mag; citation_title=Federated learning: challenges, methods, and future directions; citation_author=T Li, AK Sahu, A Talwalkar, V Smith; citation_volume=37; citation_issue=3; citation_publication_date=2020; citation_pages=50-60; citation_doi=10.1109/MSP.2020.2975749; citation_id=CR14 citation_journal_title=Proc Mach Learn Syst; citation_title=Federated optimization in heterogeneous networks; citation_author=T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith; citation_volume=2; citation_publication_date=2020; citation_pages=429-450; citation_id=CR15 Li X, Huang K, Yang W, Wang S, Zhang Z (2019) On the convergence of fedavg on non-iid data. arXiv:1907.02189 citation_journal_title=ACM Comput Surv (CSUR); citation_title=When machine learning meets privacy: a survey and outlook; citation_author=B Liu, M Ding, S Shaham, W Rahayu, F Farokhi, Z Lin; citation_volume=54; citation_issue=2; citation_publication_date=2021; citation_pages=1-36; citation_doi=10.1145/3436755; citation_id=CR17 citation_journal_title=Proc AAAI Conf Artif Intell; citation_title=Federated learning for vision-and-language grounding problems; citation_author=F Liu, X Wu, S Ge, W Fan, Y Zou; citation_volume=34; citation_publication_date=2020; citation_pages=11572-11579; citation_id=CR18 M L, Y C, Z C (2018) Transferable representation learning with deep adaptation networks. IEEE Trans Pattern Anal Mach Intell Ma X, Zhang J, Guo S, Xu W (2022a) Layer-wised model aggregation for personalized federated learning citation_title=A state-of-the-art survey on solving non-iid data in federated learning; citation_publication_date=2022; citation_id=CR21; citation_author=X Ma; citation_author=J Zhu; citation_author=Z Lin; citation_author=S Chen; citation_author=Y Qin; citation_publisher=FGCS Mansour Y, Mohri M, Ro J, Suresh AT (2020) Three approaches for personalization with applications to federated learning. Computer Science McMahan B, Moore E, Ramage D, Hampson S, y Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, PMLR, pp 1273–1282 McMahan HB, Moore E, Ramage D, y Arcas BA (2016) Federated learning of deep networks using model averaging. arXiv preprint arXiv:1602.05629 2 citation_journal_title=Futur Gener Comput Syst; citation_title=A survey on security and privacy of federated learning; citation_author=V Mothukuri, RM Parizi, S Pouriyeh, Y Huang, A Dehghantanha, G Srivastava; citation_volume=115; citation_publication_date=2021; citation_pages=619-640; citation_doi=10.1016/j.future.2020.10.007; citation_id=CR25 OpenAI (2022) Chatgpt: optimizing language models for dialogue. https://openai.com/blog/chatgpt/ Ouyang X, Xie Z, Zhou J, Huang J, Xing G (2021) Clusterfl: a similarity-aware federated learning system for human activity recognition. In: MobiSys ’21: The 19th Annual International Conference on Mobile Systems, Applications, and Services Papernot N, McDaniel P, Sinha A, Wellman M (2016) Towards the science of security and privacy in machine learning. arXiv preprint arXiv:1611.03814 citation_journal_title=IEEE Transactions on Neural Networks and Learning Systems; citation_title=Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints; citation_author=F Sattler, KR Muller, W Samek; citation_volume=PP; citation_issue=99; citation_publication_date=2020; citation_pages=1-13; citation_id=CR29 Silva S, Gutman BA, Romero E, Thompson PM, Altmann A, Lorenzi M (2019) Federated learning in distributed medical databases: Meta-analysis of large-scale subcortical brain data. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), IEEE, pp 270–274 Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Computer Science Voigt P, Von dem Bussche A (2017) The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed, Cham: Springer International Publishing 10(3152676):10–5555 Wang H, Kaplan Z, Niu D, Li B (2020) Optimizing federated learning on non-iid data with reinforcement learning. In: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications citation_journal_title=IEEE Network; citation_title=In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning; citation_author=X Wang, Y Han, C Wang, Q Zhao, M Chen; citation_volume=PP; citation_issue=99; citation_publication_date=2019; citation_pages=1-10; citation_id=CR34 Wu C, Wu F, Cao Y, Huang Y, Xie X (2021) Fedgnn: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925 citation_journal_title=ACM Trans Knowl Discov Data just Accepted; citation_title=Personalized federated learning on non-iid data via group-based meta-learning; citation_author=L Yang, J Huang, W Lin, J Cao; citation_publication_date=2022; citation_doi=10.1145/3558005; citation_id=CR36 citation_journal_title=ACM Transactions on Intelligent Systems and Technology; citation_title=Federated machine learning: Concept and applications; citation_author=Q Yang, Y Liu, T Chen, Y Tong; citation_volume=10; citation_issue=2; citation_publication_date=2019; citation_pages=1-19; citation_doi=10.1145/3298981; citation_id=CR37 Yang W, Zhang Y, Ye K, Li L, Xu CZ (2019b) Ffd: A federated learning based method for credit card fraud detection. In: Big Data–BigData 2019: 8th International Congress, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25–30, 2019, Proceedings 8, Springer, pp 18–32 citation_title=How transferable are features in deep neural networks?; citation_publication_date=2014; citation_id=CR39; citation_author=J Yosinski; citation_author=J Clune; citation_author=Y Bengio; citation_author=H Lipson; citation_publisher=MIT Press Zeiler M, Fergus R (2014) Visualizing and understanding convolutional networks. In: ECCV 2014 Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 citation_journal_title=Neurocomputing; citation_title=Machine learning on big data: Opportunities and challenges; citation_author=L Zhou, S Pan, J Wang, AV Vasilakos; citation_volume=237; citation_publication_date=2017; citation_pages=350-361; citation_doi=10.1016/j.neucom.2017.01.026; citation_id=CR42 Zhu H, Xu J, Liu S, Jin Y (2021) Federated learning on non-iid data: A survey. Neurocomputing 465:371–390 https://doi.org/10.1016/j.neucom.2021.07.098 , www.sciencedirect.com/science/article/pii/S0925231221013254