Survey of Privacy Preserving Techniques for Distributed Learning in an IoT Network

This survey provides a comprehensive review of privacy-preserving techniques applicable to distributed learning in Internet-of-Things (IoT) environments. The paper examines classical approaches, including Differential Privacy, Homomorphic Encryption, Secure Multi-party Computation, Distributed Selective Stochastic Gradient Descent, and Anonymization, as well as more recent methods such as additive and multiplicative schemes, blockchain-based mechanisms, Bloom Filter–based preprocessing, and intrusion detection systems. Each technique is analyzed with respect to its ability to protect data privacy and its suitability for deployment on resource-constrained IoT devices. Background information on IoT architectures, device limitations, and distributed learning paradigms is provided to contextualize the discussion. The survey evaluates trade-offs among computational overhead, memory usage, communication requirements, and privacy protection, and offers guidance for selecting appropriate techniques based on application requirements and device capabilities.