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Federated learning for energy constrained devices: a systematic mapping study

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Abstract

Federated machine learning (Fed ML) is a new distributed machine learning technique using clients’ local data applied to collaboratively train a global model without transmitting the datasets. Nodes, the participating devices in the ML training, only send parameter updates (e.g., weight updates in the case of neural networks), which are fused together by the server to build the global model without compromising raw data privacy. Fed ML guarantees its confidentiality by not divulging node data to third party, central servers. Privacy of data is a crucial network security aspect of Fed ML that will enable the technique for use in the context of data-sensitive Internet of Things (IoT) and mobile applications (including smart geo-location and smart grid infrastructure). However, most IoT and mobile devices are particularly energy constrained, which requires optimization of the Fed ML process for efficient training tasks and optimized power consumption. This paper, to the best of our knowledge, is the first Systematic Mapping Study (SMS) on Fed ML for energy constrained devices. First, we selected a total of 67 from 800 papers that satisfy our criteria, then provide a structured overview of the field using a set of carefully chosen research questions. Finally, we attempt to offer an analysis of the state-of-the-art Fed ML techniques and outline potential recommendations for the research community.

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Data availability

No additional data were used in the study besides the list of the papers retrieved from public databases as shown in the process description.

Notes

  1. https://gitlab.com/rachid-el-mokadem/fedmlsysrev.

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  1. Telecommunication Systems, Networks and Services Lab, National Institute of Posts and Telecommunications, Rabat, Morocco

    Rachid El Mokadem, Yann Ben Maissa & Zineb El Akkaoui

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All authors have participated in conception and the analysis of the study in addition to writing the paper. REM has also done the paper selection and classification. All authors read and approved the final manuscript.

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Appendix

Appendix

This appendix lists all papers included in our study, tagged from P1 to P67 (chronological order) (Table 5).

Table 5 Papers list
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El Mokadem, R., Ben Maissa, Y. & El Akkaoui, Z. Federated learning for energy constrained devices: a systematic mapping study. Cluster Comput 26, 1685–1708 (2023). https://doi.org/10.1007/s10586-022-03763-4

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