  Federated Machine Learning (Fed ML) is a new distributed machine learning
technique applied to collaboratively train a global model using clients local
data without transmitting it. Nodes 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. By not divulging node data, Fed ML guarantees its
confidentiality, a crucial aspect of network security, which enables it to be
used in the context of data-sensitive Internet of Things (IoT) and mobile
applications, such as smart Geo-location and the smart grid. However, most IoT
devices are particularly energy constrained, which raises the need to optimize
the Fed ML process for efficient training tasks and optimized power
consumption. In this paper, we conduct, to the best of our knowledge, the first
Systematic Mapping Study (SMS) on Fed ML optimization techniques for
energy-constrained IoT devices. From a total of more than 800 papers, we select
67 that satisfy our criteria and give a structured overview of the field using
a set of carefully chosen research questions. Finally, we attempt to provide an
analysis of the energy-constrained Fed ML state of the art and try to outline
some potential recommendations for the research community.
