| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
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