Fair and autonomous sharing of federate learning models in mobile Internet of Things

TL;DR

This paper proposes a privacy-preserving, autonomous, and fair model sharing protocol for mobile federated learning using smart contracts and IPFS, addressing trust and fairness challenges in edge environments.

Contribution

It introduces a novel decentralized model sharing protocol leveraging smart contracts and IPFS, ensuring fairness and autonomy without third-party reliance in mobile federated learning.

Findings

The protocol achieves efficient execution times of around 0.05 seconds per step.

It enables autonomous and fair model sharing among mobile devices.

Experimental results validate the protocol's effectiveness and efficiency.

Abstract

Federate learning can conduct machine learning as well as protect the privacy of self-owned training data on corresponding ends, instead of having to upload to a central trusted data aggregation server. In mobile scenarios, a centralized trusted server may not be existing, and even though it exists, the delay will not be manageable, e.g., smart driving cars. Thus, mobile federate learning at the edge with privacy-awareness is attracted more and more attentions. It then imposes a problem - after data are trained on a mobile terminal to obtain a learned model, how to share the model parameters among others to create more accurate and robust accumulative final model. This kind of model sharing confronts several challenges, e.g., the sharing must be conducted without a third trusted party (autonomously), and the sharing must be fair as model training (by training data)is valuable. To tackle the above challenges, we propose a smart contract and IPFS (Inter-Planetary File System) based model sharing protocol and algorithms to address the challenges. The proposed protocol does not rely on a trusted third party, where individual-learned models are shared/stored in corresponding ends. Conducted through extensive experiments, three main steps of the proposed protocol are evaluated. The average executive time of the three steps are 0.059s, 0.060s and 0.032s, demonstrating its efficiency.