  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.
