Privacy-Preserving Federal Learning Chain for Internet of Things

Abstract

The expansion of Internet of Things (IoT) spawns large on-device machine learning demands, while the machine learning can be a hard task for resource constrained IoT terminals with fragmented dataset. Federal learning (FL), which aims to build a joint model across multiple devices, IoT-FL has now become a promising path for learning on terminals. In broad FL fields, current server-client pattern cannot jump out of the third party self-trustless problem, and recent researches suggest that even sharing training results may also reveal the raw data sets. Homomorphic encryption (HE) is a powerful method in privacy preserving, while so far it is hard to applied HE into multi-party computing (MPC) scenarios including FL. Combining with the existing IoT architecture, in this paper, we customize a scheme (FL chain) dedicated for the privacy and trustiness issues in IoT-FL scenarios, which integrates blockchain smart contract and HE. Differ from traditional schemes, our FL chain is highly adaptive with current IoT architecture and it is the first scheme that applied HE into IoT-FL privacy preserving. Theoretical analysis and experimental results prove the feasibility of FL chain.