Hybrid Federated Learning and Blockchain Ecosystems for Secure Edge Intelligence
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706013Keywords:
Federated Learning; Blockchain, Edge Intelligence, Secure Distributed Learning, Smart Contracts, Decentralized Training, IoT Security, Privacy-Preserving AI, Consensus Mechanisms, Hybrid FL ArchitectureAbstract
The rapid proliferation of edge devices and distributed IoT infrastructures has heightened the demand for secure, privacy-preserving, and scalable intelligence at the network edge. Federated Learning (FL) has emerged as a transformative approach that enables collaborative model training without centralized data aggregation; however, traditional FL suffers from vulnerabilities such as poisoning attacks, unreliable device participation, single-point aggregation risks, and limited transparency. To address these gaps, this paper proposes a novel Hybrid Federated Learning and Blockchain Ecosystem designed to enhance the security, trustworthiness, and resilience of edge intelligence systems. The framework integrates blockchain-based decentralized consensus with FL model updates, ensuring immutability, auditability, and tamper-proof coordination among heterogeneous edge nodes. Smart contracts are used to automate model aggregation, reward honest contributions, and verify update integrity, while a hybrid training architecture combines hierarchical FL, peer-to-peer coordination, and local differential privacy to optimize efficiency and reduce communication overhead. Experimental evaluation across large-scale edge network simulations demonstrates that the proposed hybrid ecosystem significantly improves security against poisoning attacks, increases model accuracy under adversarial conditions, reduces aggregation latency, and achieves higher robustness compared to conventional FL approaches. The results highlight the potential of blockchain-enhanced federated learning as a key enabler of secure, accountable, and high-performance edge intelligence for next-generation IoT environments.
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