Efficient and Privacy-Preserving Artificial Neural Network Model Training With Separated Data in IoT

Machine learning models based on artificial neural networks (ANNs) have been widely adopted to support diverse complex applications. However, the training of such models heavily relies on large-scale datasets, which may raise privacy concerns. Taking the Internet of Thingss (IoTs) as an example, distributed edge devices collect data, while central servers aggregate this data for model training and in-depth analysis. Unlike frameworks like federated learning (FL), local model training becomes infeasible due to constrained edge device capabilities, model confidentiality requirements, or the separation of complete data features across multiple devices. This scenario may render traditional FL-based privacy-preserving frameworks ineffective. To address these limitations, we propose an efficient privacy-preserving model training protocol that demonstrates practical advantages over fully homomorphic encryption (FHE) and functional encryption methods. Our protocol leverages additively homomorphic encryption combined with the chinese remainder theorem (CRT) to design secure matrix-vector computations, minimizing encryption/decryption operations and reducing computational overhead on edge devices while preserving training efficiency and model accuracy. To further optimize performance, we further propose a secondary protocol that introduces optimization strategies to enhance efficiency and lighten edge nodes’ computational burdens. Security analysis and rigorous correctness proofs are provided, and performance evaluations and experimental validation confirm both protocols’ practical feasibility and effectiveness.