Federated learning for energy constrained devices: a systematic mapping study

• Published in: Cluster computing 2023-04, Vol.26 (2), p.1685-1708
• Main Authors: El Mokadem, Rachid, Ben Maissa, Yann, El Akkaoui, Zineb
• Format: Article
• Language: English
• Subjects: Algorithms; Applications programs; Artificial intelligence; Communication; Computer Communication Networks; Computer Science; Energy consumption; Federated learning; Internet of Things; Literature reviews; Machine learning; Mapping; Mobile computing; Neural networks; Operating Systems; Optimization; Power consumption; Privacy; Processor Architectures; Security aspects; Smart cities; Smart grid; Software engineering; Systematic review; Unmanned aerial vehicles
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-022-03763-4

Federated machine learning (F ed 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. F ed ML guarantees its confidentiality by not divulging node data to third party, central servers. Privacy of data is a crucial network security aspect of F ed 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 F ed 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 F ed 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 F ed ML techniques and outline potential recommendations for the research community.