Decentralized Federated Learning Framework Enhances IoT Privacy and Security ∞ Research

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Briefing

The proliferation of IoT devices necessitates secure, privacy-preserving machine learning, yet traditional centralized and existing federated learning (FL) models often fall short in safeguarding sensitive data and ensuring verifiable operations. This research introduces FL-DABE-BC, a groundbreaking framework that seamlessly integrates Decentralized Attribute-Based Encryption (DABE) for fine-grained access control, Homomorphic Encryption (HE) and Secure Multi-Party Computation (SMPC) for privacy-preserving computations, and blockchain technology for immutable record-keeping and transparent communication. This holistic architecture establishes a new paradigm for trustworthy, scalable, and privacy-centric AI development in decentralized IoT ecosystems, fundamentally reshaping how sensitive data is processed and secured at the edge.

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Context

Before this research, Federated Learning (FL) offered a promising approach to distributed AI by allowing models to train on local data without centralizing raw information. However, FL’s inherent security and privacy limitations, particularly in resource-constrained and data-sensitive IoT environments, presented a significant foundational problem. Challenges included ensuring decentralized authentication, protecting data during aggregation, maintaining communication integrity, and providing verifiable audit trails, which hindered its widespread adoption for critical applications.

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Analysis

The FL-DABE-BC framework introduces a multi-layered cryptographic and distributed ledger model for federated learning in IoT. Its core mechanism involves encrypting raw IoT data locally using Decentralized Attribute-Based Encryption (DABE), which grants access based on specific attributes rather than individual identities, thereby decentralizing access control. Model updates are then processed using Homomorphic Encryption (HE) and Secure Multi-Party Computation (SMPC) within fog layers, allowing computations on encrypted data without revealing the underlying sensitive information.

A blockchain network underpins the entire process, serving as an immutable, transparent ledger for all model transactions, updates, and secure communications, ensuring data integrity and decentralized authentication across the IoT, fog, and cloud layers. This approach fundamentally differs by providing an integrated, end-to-end security and privacy solution, moving beyond isolated cryptographic applications to a cohesive, verifiable ecosystem.

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Parameters

Core Concept ∞ Federated Learning Framework
New System/Protocol ∞ FL-DABE-BC
Key Cryptographic Primitives ∞ Decentralized Attribute-Based Encryption (DABE), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), Differential Privacy (DP)
Underlying Technology ∞ Blockchain
Target Environment ∞ Internet of Things (IoT) Scenarios
Formal Analysis Method ∞ BAN Logic
Key Authors ∞ Narkedimilli, S. et al.

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Outlook

This framework opens new research avenues for optimizing cryptographic primitives, such as enhancing DABE efficiency and refining differential privacy mechanisms to achieve a better balance between data leakage prevention and model accuracy. In the next 3-5 years, this theory could unlock truly privacy-preserving AI applications in smart healthcare, industrial automation, and smart cities, where sensitive IoT data can be leveraged for collective intelligence without compromising individual privacy. Further research will focus on scalability testing in real-world, large-scale IoT deployments and developing automated tools for microservice management to ease deployment across diverse environments.

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Verdict

FL-DABE-BC establishes a foundational blueprint for building inherently secure and privacy-preserving decentralized AI systems, critical for the future of trustless data economies.

Signal Acquired from ∞ arxiv.org