The benefit associated with the deployment of Internet of Things (IoT) technology is increasing daily. IoT has revolutionized our ways of life, especially when we consider its applications in smart connected homes. Smart devices at home enable the collection of data from multiple sensors for a range of applications and services. Nevertheless, the security and privacy issues associated with aggregating multiple sensors’ data in smart connected homes have not yet been sufficiently prioritized. Along this development, this paper proposes HOMEFUS, a privacy and security-aware model that leverages information theoretic correlation analysis and gradient boosting to fuse multiple sensors’ data at the edge nodes of smart connected homes. HOMEFUS employs federated learning, edge and cloud computing to reduce privacy leakage of sensitive data. To demonstrate its applicability, we show that the proposed model meets the requirements for efficient data fusion pipelines. The model guides practitio ners and researchers on how to setup secure smart connected homes that comply with privacy laws, regulations, and standards.