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Title	Fl-Dabe-Bc: A Privacy-Enhanced Decentralized Authentication And Secure Communication Framework For Fl In Iot-Enabled Smart Cities
ID_Doc	26614
Authors	Narkedimilli S.; Pravisha P.; Sriram A.V.; Raghav S.; Vangapandu P.
Year	2025
Published	FMSys 2025 - Proceedings of the 2025 2nd International Workshop on Foundation Models for Cyber-Physical Systems and Internet of Things, 2025 Cyber-Physical Systems and Internet-of-Things Week, CPS-IoT Week 2025 Workshops
DOI	http://dx.doi.org/10.1145/3722565.3727194
Abstract	Federated Learning (FL) offers a distributed approach to machine learning that preserves data privacy by avoiding the exchange of sensitive IoT sensor information. This paper introduces a novel IoT framework that integrates advanced security tools to tackle key privacy and security challenges. It employs Decentralized Attribute-Based Encryption (DABE) for decentralized authentication and data encryption, Homomorphic Encryption (HE) for secure computations on encrypted data, Secure Multi-Party Computation (SMPC) for collaborative processing, and Blockchain for distributed ledger management and transparent communication. In this system, IoT devices encrypt data locally with DABE, while initial model training occurs on cloud servers within an immutable blockchain network that supports peer-to-peer authentication. Encrypted model weights are then transferred to the fog layer via HE and aggregated using SMPC, after which the FL server updates and distributes the global model to the IoT devices. This innovative framework effectively addresses the challenges of secure decentralized learning, enabling privacy-preserving, efficient, and secure federated learning for IoT applications and real-time analytics. © 2025 Copyright held by the owner/author(s).
Author Keywords	Blockchain; Decentralized Attribute-Based Encryption; Federated Learning; FL-DABE-BC

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