Toward Secure and Private Federated Learning for IoT using Blockchain

Abstract

Recent advances in the Internet of Things (IoT) offer a plethora of new opportunities for several intelligent services and applications. As the IoT connects a massive number of devices, inevitable security threats must be addressed. On the one hand, machine learning (ML), especially federated learning (FL), is proposed as a promising distributed ML paradigm to improve attack detection performance in the IoT network due to its privacy-preserving and lower latency advantages. On the other hand, blockchain is proposed as a decentralized technology to establish a secure and decentralized environment for IoT devices. However,$F$L and blockchain solutions are not well suited for the IoT context that suffers from resource limitations, such as limited communication bandwidth and scarce computing resources of IoT devices. In addition, traditional FL and blockchain solutions are unable to guarantee the reliability of data. In this paper, we present a decentralized FL framework powered by blockchain for security attack protection in IoT systems. In addition, we propose an oracle blockchain network that protects privacy and guarantees data reliability. The oracle blockchain acts as a trusted third party to verify the reliability of data and pattern formation at the network edge. Finally, we will formulate a resource allocation problem to allocate the necessary bandwidth to selected devices meticulously. The goal is to minimize communication between devices in the framework and prioritize devices with reliable behavior.