2024/10/26 by Sathwik Narkedimilli, Narkedimilli, Sathwik, Sriram, Amballa Venkata +1
Computer Science · #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2410.20259
openalex publication_date 2024/10/26 · openalex created_date 2024/11/14 · openalex updated_date 2026/07/28
This study proposes an advanced Federated Learning (FL) framework designed to enhance data privacy and security in IoT environments by integrating Decentralized Attribute-Based Encryption (DABE), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), and Blockchain technology. Unlike traditional FL, our framework enables secure, decentralized authentication and encryption directly on IoT devices using DABE, allowing sensitive data to remain locally encrypted. Homomorphic Encryption permits computations on encrypted data, and SMPC ensures privacy in collaborative computations, while Blockchain technology provides transparent, immutable record-keeping for all transactions and model updates. Local model weights are encrypted and transmitted to fog layers for aggregation using HE and SMPC, then iteratively refined by the central server using differential privacy to safeguard against data leakage. This secure, privacy-preserving FL framework delivers a robust solution for efficient model training and real-time analytics across distributed IoT devices, offering significant advancements in secure decentralized learning for IoT applications.