On a Framework for Federated Cluster Analysis
Source abstract
Abstract
Federated learning is becoming increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data. Many works focus on supervised learning, while the area of federated unsupervised learning, similar to federated clustering, is still less explored. In this paper, we introduce a federated clustering framework that solves three challenges: determine the number of global clusters in a federated dataset, obtain a partition of the data via a federated fuzzy c-means algorithm, and validate the clustering through a federated fuzzy Davies–Bouldin index. The complete framework is evaluated through numerical experiments on artificial and real-world datasets. The observed results are promising, as in most cases the federated clustering framework’s results are consistent with its nonfederated equivalent. Moreover, we embed an alternative federated fuzzy c-means formulation into our framework and observe that our formulation is more reliable in case the data are noni.i.d., while the performance is on par in the i.i.d. case.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Privacy-Preserving Technologies in Data · Human Mobility and Location-Based Analysis · Recommender Systems and Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, 6229 EN Maastricht, The Netherlands
References (43)
Show source-provided references
1. EU (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data (...) (General Data Protection Regulation). Off. J. Eur. Union, 119, 1–88.
2. Advances and Open Problems in Federated Learning10.1561/2200000083 ↗
3. Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D. (2018). Federated learning for mobile keyboard prediction. arXiv.
4. Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach10.1109/ACCESS.2020.2968399 ↗
5. Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: EuroCAT
6. Federated learning of predictive models from federated Electronic Health Records10.1016/j.ijmedinf.2018.01.007 ↗
7. Grefen, P., Ludwig, H., Tata, S., Dijkman, R., Baracaldo, N., Wilbik, A., and D’hondt, T. (2018). Complex collaborative physical process management: A position on the trinity of BPM, IoT and DA. IFIP Advances in Information and Communication Technology, Proceedings of the Working Conference on Virtual Enterprises, Cardiff, UK, 17–19 September 2018, Springer.10.1007/978-3-319-99127-6_21 ↗
8. Duan, M., Liu, D., Chen, X., Tan, Y., Ren, J., Qiao, L., and Liang, L. (2019, January 17–20). Astraea: Self-Balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications. Proceedings of the 2019 IEEE 37th International Conference on Computer Design (ICCD), Abu Dhabi, United Arab Emirates.10.1109/ICCD46524.2019.00038 ↗
9. In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning10.1109/MNET.2019.1800286 ↗
10. A Comprehensive Survey of Privacy-Preserving Federated Learning: A Taxonomy, Review, and Future Directions10.1145/3460427 ↗
11. Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges10.1109/COMST.2021.3090430 ↗
12. Cluster analysis and related techniques in medical research10.1177/096228029200100103 ↗
13. Research on social data by means of cluster analysis10.1016/j.aci.2018.02.003 ↗
14. Applications of cluster analysis to the creation of perfectionism profiles: A comparison of two clustering approaches10.3389/fpsyg.2014.00343 ↗
15. The application of cluster analysis in strategic management research: An analysis and critique10.1002/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G ↗
16. Cluster analysis in marketing research: Review and suggestions for application10.1177/002224378302000204 ↗
17. Understanding the domestic market using cluster analysis: A case study of the marketing efforts of Travel Alberta10.1177/135676670200800305 ↗
18. Methodology review: Clustering methods10.1177/014662168701100401 ↗
19. Kumar, H.H., Karthik, V.R., and Nair, M.K. (2020, January 6–7). Federated K-Means Clustering: A Novel Edge AI Based Approach for Privacy Preservation. Proceedings of the 2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), Bengaluru, India.10.1109/CCEM50674.2020.00021 ↗
20. Federated FCM: Clustering Under Privacy Requirements10.1109/TFUZZ.2021.3105193 ↗
21. Bárcena, J.L.C., Marcelloni, F., Renda, A., Bechini, A., and Ducange, P. (2021, January 20–22). A Federated Fuzzy c-means Clustering Algorithm. Proceedings of the International Workshop on Fuzzy Logic and Applications (WILF 2021), Vietri sul Mare, Italy.
22. Heterogeneity for the Win: One-Shot Federated Clustering
23. Hastie, T., Tibshirani, R., and Friedman, J. (2017). The Elements of Statistical Learning—Data Mining, Inference and Prediction, Springer.
24. Arthur, D., and Vassilvitskii, S. (2007, January 7–9). K-Means++: The Advantages of Careful Seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms; Society for Industrial and Applied Mathematics, SODA ’07, New Orleans, LA, USA.
25. Scikit-learn: Machine Learning in Python
26. FCM: The fuzzy c-means clustering algorithm10.1016/0098-3004(84)90020-7 ↗
27. Reducing the time complexity of the fuzzy c-means algorithm10.1109/91.995126 ↗
28. Fuzzy C- Means Algorithm—A Review
29. Steinbach, M., Ertöz, L., and Kumar, V. (2004). The challenges of clustering high dimensional data. New Directions in Statistical Physics, Springer.10.1007/978-3-662-08968-2_16 ↗
30. Fuzzy C-Means in High Dimensional Spaces
31. A Cluster Separation Measure10.1109/TPAMI.1979.4766909 ↗
32. Vergani, A.A., and Binaghi, E. (2018, January 8–13). A Soft Davies–Bouldin Separation Measure. Proceedings of the 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Rio de Janeiro, Brazil.10.1109/FUZZ-IEEE.2018.8491581 ↗
33. An Efficient Framework for Clustered Federated Learning
34. Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints10.1109/TNNLS.2020.3015958 ↗
35. Kim, Y., Hakim, E.A., Haraldson, J., Eriksson, H., da Silva, J.M.B., and Fischione, C. (2021, January 14–23). Dynamic Clustering in Federated Learning. Proceedings of the ICC 2021—IEEE International Conference on Communications, Montreal, QC, Canada.10.1109/ICC42927.2021.9500877 ↗
36. Xie, M., Long, G., Shen, T., Zhou, T., Wang, X., Jiang, J., and Zhang, C. (2021). Multi-center federated learning. arXiv.
37. Stallmann, M., and Wilbik, A. (2022, January 1). Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM). Proceedings of the International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022 (FL-AAAI-22), Vancouver, BC, Canada.
38. McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B.A. (2017, January 20–22). Communication-efficient learning of deep networks from decentralized data. Proceedings of the Artificial Intelligence and Statistics, PMLR, Fort Lauderdale, FL, USA.
39. EBK-means: A clustering technique based on elbow method and k-means in WSN
40. An examination of procedures for determining the number of clusters in a data set10.1007/BF02294245 ↗
41. Fränti, P., and Sieranoja, S. (2022, March 27). Clustering Basic Benchmark. Available online: http://cs.uef.fi/sipu/datasets/.
42. XNN graph
43. A data-driven approach to predict the success of bank telemarketing10.1016/j.dss.2014.03.001 ↗