Federated learning for energy constrained devices: a systematic mapping study

Abstract

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 FedML 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.

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

“…In [45], Liu et al focus on the concept of vertical federated learning and its challenges. The study in [17] provides a systematic mapping study highlighting why federated learning has been used and different machine learning pipelines used for federated learning, while a systematic mapping study focused on energy-constrained IoT devices [18].…”

“…These challenges are important in the FL environment, and many studies in the literature have been proposed to address them. There are a few mapping studies on the federated learning environment; for example, the study in [17] covers the motivation for using FL, and the study in [18] focuses on applying federated learning on energy-constrained IoT devices. However, there are no comprehensive studies that aim to provide a systematic mapping study to cover the techniques utilized to provide communication efficiency and overcome the non-IID data challenge.…”