A Joint Survey in Decentralized Federated Learning and TinyML: A Brief Introduction to Swarm Learning

TinyML/DL is a new subfield of ML that allows for the deployment of ML algorithms on low-power devices to process their own data. The lack of resources restricts the aforementioned devices to running only inference tasks (static TinyML), while training is handled by a more computationally efficient system, such as the cloud. In recent literature, the focus has been on conducting real-time on-device training tasks (Reformable TinyML) while being wirelessly connected. With data processing being shift to edge devices, the development of decentralized federated learning (DFL) schemes becomes justified. Within these setups, nodes work together to train a neural network model, eliminating the necessity of a central coordinator. Ensuring secure communication among nodes is of utmost importance for protecting data privacy during edge device training. Swarm Learning (SL) emerges as a DFL paradigm that promotes collaborative learning through peer-to-peer interaction, utilizing edge computing and blockchain technology. While SL provides a robust defense against adversarial attacks, it comes at a high computational expense. In this survey, we emphasize the current literature regarding both DFL and TinyML/DL fields. We explore the obstacles encountered by resource-starved devices in this collaboration and provide a brief overview of the potential of transitioning to Swarm Learning.