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A Docker-based federated learning framework design and deployment for multi-modal data stream classification

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Abstract

In the high-performance computing (HPC) domain, federated learning has gained immense popularity. Especially in emotional and physical health analytics and experimental facilities. Federated learning is one of the most promising distributed machine learning frameworks because it supports data privacy and security by not sharing the clients’ data but instead sharing their local models. In federated learning, many clients explicitly train their machine learning/deep learning models (local training) before aggregating them as a global model at the global server. However, the FL framework is difficult to build and deploy across multiple distributed clients due to its heterogeneous nature. We developed Docker-enabled federated learning (DFL) by utilizing client-agnostic technologies like Docker containers to simplify the deployment of FL frameworks for data stream processing on the heterogeneous client. In the DFL, the clients and global servers are written using TensorFlow and lightweight message queuing telemetry transport protocol to communicate between clients and global servers in the IoT environment. Furthermore, the DFL’s effectiveness, efficiency, and scalability are evaluated in the test case scenario where real-time emotion state classification is done from distributed multi-modal physiological data streams under various practical configurations.

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Availability of data and materials

Publically available DEAP dataset [38].

Code availability

https://github.com/officialarijit/Fed-ReMECS-Docker.

Notes

  1. DFL’s source code: https://github.com/officialarijit/Fed-ReMECS-Docker.

  2. DockerHub: https://hub.docker.com/.

  3. Fed-clients: https://hub.docker.com/repository/docker/arijitnandi/fedclient.

  4. Fed-server: https://hub.docker.com/repository/docker/arijitnandi/fedserver.

  5. https://www.tensorflow.org/.

  6. https://github.com/emqx/emqx.

  7. https://www.emqx.io/.

  8. DEAP dataset link: https://www.eecs.qmul.ac.uk/mmv/datasets/deap/.

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Acknowledgements

Arijit Nandi is a fellow of Eurecat’s "Vicente López" PhD grant program. This study has been partially funded by ACCIÓ, Spain (Pla d’Actuació de Centres Tecnológics 2021) under the project TutorIA. We would like to thank the authors of DEAP dataset [38] for sharing with us.

Funding

Project TutorIA, ACCIÓ, Generalitat de Catalunya, Spain.

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Authors and Affiliations

  1. Department of CS, Universitat Politècnica de Catalunya, 08034, Barcelona, Spain

    Arijit Nandi & Fatos Xhafa

  2. Eurecat, Centre Tecnològic de Catalunya, 08005, Barcelona, Spain

    Arijit Nandi & Rohit Kumar

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All authors contributed to designing the model and the computational framework, implementation, analysis of the results and writing of the manuscript.

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Correspondence to Arijit Nandi or Fatos Xhafa.

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Nandi, A., Xhafa, F. & Kumar, R. A Docker-based federated learning framework design and deployment for multi-modal data stream classification. Computing 105, 2195–2229 (2023). https://doi.org/10.1007/s00607-023-01179-5

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