Abstract
The article suggests an approach to implementing a federated machine learning framework for the Java platform. The subject of the research is federated machine learning, a brief description is given in the introduction, and the object is frameworks for federated machine learning systems development. As an experiment, it is shown the possibility of running a system with Java FL client for training linear ANN with one hidden layer with DeepLearning4Java backend together with Python FL client with PyTorch backend. The results model’s accuracies are compared.
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References
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., Ramage, D.: Federated learning for mobile keyboard prediction (2018). arXiv:1811.03604
Google LLC: TensorFlow Federated GitHub repository. https://github.com/tensorflow/federated
Webank’s AI Department: FATE (Federated AI Technology Enabler) framework GitHub repository. https://github.com/FederatedAI/FATE
PaddlePaddle Baidu: PaddleFL framework GitHub repository. https://github.com/PaddlePaddle/PaddleFL
OpenMined: PySyft framework GitHub repository. https://github.com/OpenMined/PySyft
Kholod, I., Yanaki, E., Fomichev, D., Shalugin, E., Novikova, E., Filippov, E., Nordlund, M.: Open-source federated learning frameworks for iot: a comparative review and analysis. Sensors 21(1), 167 (2021)
Adap GmbH: Flower federated learning framework GitHub repository. https://github.com/adap/flower
Beutel, D.J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., Lane, N.D.: Flower: a friendly federated learning research framework (2020). arXiv:2007.14390
Scaleout: FEDn framework GitHub repository. https://github.com/scaleoutsystems/fedn
Ekmefjord, M., Ait-Mlouk, A., Alawadi, S., Åkesson, M., Stoyanova, D., Spjuth, O., Toor, S., Hellander, A.: Scalable federated machine learning with fedn (2021)
Efremov, M.A., Kholod, I.I., Kolpaschikov, M.A.: Java federated learning framework architecture. In: 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), pp. 306–309 (2021). https://doi.org/10.1109/ElConRus51938.2021.9396508
Eclipse Foundation: DL4J — Open-source, distributed, deep learning library for the JVM GitHub repository. https://github.com/eclipse/deeplearning4j/
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR (2017)
Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., Khazaeni, Y.: Federated learning with matched averaging (2020). arXiv:2002.06440
Kholod, I., Shorov, A., Gorlatch, S.: Efficient distribution and processing of data for parallelizing data mining in mobile clouds. J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl. 11(1), 2–17 (2020)
Smartilizer Scandinavia AB.: Commercial vehicles sensor data set – 2 dumpers collected timestamp, speed, gyroscope, and accelerometer data. https://www.kaggle.com/smartilizer/commercial-vehicles-sensor-data-set
Acknowledgements
We thank Smartilizer Scandinavia AB for provided dataset and Yandex Cloud service for the platform for experiments computation purpose.
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Kholod, I.I., Efremov, M.A., Kolpashikov, M.A., Vasilyev, A.V., Tabakov, P.L., Aristarhov, I.E. (2022). FL4J—Federated Learning Framework for Java. In: Camacho, D., Rosaci, D., Sarné, G.M.L., Versaci, M. (eds) Intelligent Distributed Computing XIV. IDC 2021. Studies in Computational Intelligence, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-96627-0_21
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DOI: https://doi.org/10.1007/978-3-030-96627-0_21
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