Tiny Federated Learning with Blockchain for Privacy and Security Preservation of MCU-Based IoT Applications

In several Internet of Things (IoT) application contexts, such as autonomous vehicles, healthcare, and smart cities, massive amounts of data are produced at the edge and used in neural networks deployed in central servers or the cloud. On the other hand, physical or legal constraints may restrict the use of this data only locally. Thus, the development of secure and efficient traditional Machine Learning solutions in the IoT context can be a huge challenge. Therefore, this paper combines an approach based on Tiny Federated Learning and Transfer Learning with on-board training, as an effective paradigm to continuously analyze data locally without having to transfer sensitive data to untrusted servers and networks. Moreover, a decentralized blockchain-based federated learning framework is implemented to provide tamper-proof data protection and resistance to malicious or compromised tiny devices. A prototype is created based on the Hyperledger Fabric and real resource-constrained microcontrollers to assess the viability of the proposed solution.