Abstract
Anomaly detection is an important task to identify rare events such as fraud, intrusions, or medical diseases. However, it often needs to be applied on personal or otherwise sensitive data, e.g. business data. This gives rise to concerns regarding the protection of the sensitive data, especially if it is to be analysed by third parties, e.g. in collaborative settings, where data is collected by different entities, but shall be analysed together to benefit from more effective models.
Besides various approaches for e.g. data anonymisation, one approach for privacy-preserving data mining is Federated Learning – especially in settings where data is collected in several distributed locations. A common, global model is obtained by aggregating models trained locally on each data source, while the training data remains at the source. Therefore, data privacy and machine learning can coexist in a decentralised system. While Federated Learning has been studied for several machine learning settings, such as classification, it is still rather unexplored for anomaly detection tasks. As anomalies are rare, they are not picked up easily by a detection method, and the representation in the model dedicated to recognise them might be lost during model aggregation.
In this paper, we thus study anomaly detection task on two different benchmark datasets, in supervised, semi-supervised, and unsupervised settings. We federate Multi-Layer Perceptrons, Gaussian Mixture Models, and Isolation Forests, and compare them to a centralised approach.
Keywords
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Konečný, J., McMahan, H.B., Yu, F.X., Richtarik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. In: Workshop on Private Multi-party Machine Learning, Conference on Neural Information Processing Systems (NIPS) (2016)
Kairouz, P., McMahan, H.B., et al.: Advances and open problems in federated learning. Found. Trends Mach. Learn. 14(1–2), 1–210 (2021)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1–58 (2009)
Hawkins, D.M.: Identification of Outliers. Springer, Dordrecht (1980). https://doi.org/10.1007/978-94-015-3994-4
Hodge, V., Austin, J.: A survey of outlier detection methodologies. Arti. Intel. Rev. 22(2), 85–126 (2004). https://doi.org/10.1023/B:AIRE.0000045502.10941.a9
Goldstein, M., Uchida, S.: A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLOS ONE 11(4), e0152 (2016)
Mendes, R., Vilela, J.P.: Privacy-preserving data mining: methods, metrics, and applications. IEEE Access 5, 10562–10582 (2017). https://doi.org/10.1109/ACCESS.2017.2706947
Hittmeir, M., Ekelhart, A., Mayer, R.: On the utility of synthetic data: an empirical evaluation on machine learning tasks. In: 2019 International Conference on Availability, Reliability and Security, Canterbury, UK. ACM (2019)
Mayer, R., Hittmeir, M., Ekelhart, A.: Privacy-preserving anomaly detection using synthetic data. In: Singhal, A., Vaidya, J. (eds.) DBSec 2020. LNCS, vol. 12122, pp. 195–207. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49669-2_11
McMahan, B., Moore, E., Ramage, D., et al.: Communication-efficient learning of deep networks from decentralized data. In: 2017 International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA. PMLR (2017)
Sheller, M.J., Reina, G.A., Edwards, B., Martin, J., Bakas, S.: Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 92–104. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11723-8_9
Pustozerova, A., Rauber, A., Mayer, R.: Training effective neural networks on structured data with federated learning. In: Barolli, L., Woungang, I., Enokido, T. (eds.) AINA 2021. LNNS, vol. 226, pp. 394–406. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75075-6_32
Silva, S., et al.: Federated learning in distributed medical databases: meta-analysis of large-scale subcortical brain data. Technical report, Inria & Université Cote d’Azur, France (2018)
Mills, J., Hu, J., Min, G.: Communication-efficient federated learning for wireless edge intelligence in IoT. IEEE IoT J. 7(7), 5986–5994 (2020)
Vasilomanolakis, E., Karuppayah, S., Mühlhäuser, M., Fischer, M.: Taxonomy and survey of collaborative intrusion detection. ACM Comput. Surv. 47(4), 1–33 (2015)
Weiss, K., Khoshgoftaar, T.M., Wang, D.D.: A survey of transfer learning. J. Big Data 3(1), 1–40 (2016). https://doi.org/10.1186/s40537-016-0043-6
Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. arXiv arXiv:1901.03407 (2019)
Chen, C., Gong, Y., Tian, Y.: Semi-supervised learning methods for network intrusion detection. In: International Conference on Systems, Man and Cybernetics, October 2008. IEEE (2008)
Reynolds, D.: Gaussian mixture models. In: Li, S.Z., Jain, A. (eds.) Encyclopedia of Biometrics. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-73003-5_196
Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation forest. In: 2008 8th IEEE International Conference on Data Mining, Pisa, Italy. IEEE (2008)
Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation-based anomaly detection. ACM Trans. Knowl. Disc. Data 6(1), 1–39 (2012)
Liu, Y., et al.: Federated forest. IEEE Trans. Big Data (2020). https://doi.org/10.1109/TBDATA.2020.2992755
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How To backdoor federated learning. In: 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Italy. PMLR (2020)
Sattler, F., Wiedemann, S., Muller, K.-R., Samek, W.: Robust and communication-efficient federated learning from non-i.i.d. data. IEEE Trans. Neural Netw. Learn. Syst. 31(9), 3400–3413 (2020). https://doi.org/10.1109/TNNLS.2019.2944481
Pustozerova, A., Mayer, R.: Information leaks in federated learning. In: Proceedings 2020 Workshop on Decentralized IoT Systems and Security, San Diego, CA. Internet Society (2020)
Choquette-Choo, C.A., Tramer, F., Carlini, N., Papernot, N.: Label-only membership inference attacks. In: International Conference on Machine Learning (PMLR) (2021)
Acknowledgments
This work was partially funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 826078 (Project ‘FeatureCloud’). This publication reflects only the authors’ view and the European Commission is not responsible for any use that may be made of the information it contains. SBA Research (SBA-K1) is a COMET Centre within the COMET – Competence Centers for Excellent Technologies Programme and funded by BMK, BMDW, and the federal state of Vienna. The COMET Programme is managed by FFG.
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Cavallin, F., Mayer, R. (2022). Anomaly Detection from Distributed Data Sources via Federated Learning. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-030-99587-4_27
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DOI: https://doi.org/10.1007/978-3-030-99587-4_27
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