FL-DABE-BC: A Privacy-Enhanced, Decentralized Authentication, and Secure Communication for Federated Learning Framework with Decentralized Attribute-Based Encryption and Blockchain for IoT Scenarios

TL;DR

This paper introduces a secure, privacy-preserving federated learning framework for IoT that combines decentralized attribute-based encryption, homomorphic encryption, secure multi-party computation, and blockchain to enhance data security and transparency.

Contribution

It presents a novel federated learning framework integrating DABE, HE, SMPC, and blockchain for secure, decentralized IoT data processing and model training.

Findings

Enables secure, decentralized authentication on IoT devices.

Supports encrypted data computation with homomorphic encryption.

Provides transparent, immutable transaction records via blockchain.

Abstract

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.