FedRule: Federated Rule Recommendation System with Graph Neural Networks

TL;DR

FedRule is a privacy-preserving federated learning system using graph neural networks to recommend smart home device rules, outperforming traditional methods while maintaining user privacy.

Contribution

Introduces FedRule, a novel federated graph neural network approach for rule recommendation in smart homes, addressing privacy concerns and leveraging graph structures.

Findings

FedRule achieves comparable performance to centralized methods.

Outperforms conventional rule recommendation solutions.

Maintains user privacy through federated training.

Abstract

Much of the value that IoT (Internet-of-Things) devices bring to ``smart'' homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera triggering a smart lock to unlock a door. Manually setting up these rules for smart devices or applications, however, is time-consuming and inefficient. Rule recommendation systems can automatically suggest rules for users by learning which rules are popular based on those previously deployed (e.g., in others' smart homes). Conventional recommendation formulations require a central server to record the rules used in many users' homes, which compromises their privacy and leaves them vulnerable to attacks on the central server's database of rules. Moreover, these solutions typically leverage generic user-item matrix methods that do not fully exploit the structure of the rule recommendation problem. In this paper, we propose a new rule recommendation system, dubbed as FedRule, to address these challenges. One graph is constructed per user upon the rules s/he is using, and the rule recommendation is formulated as a link prediction task in these graphs. This formulation enables us to design a federated training algorithm that is able to keep users' data private. Extensive experiments corroborate our claims by demonstrating that FedRule has comparable performance as the centralized setting and outperforms conventional solutions.