2022· International Journal of Data Engineering and Intelligent Computing· Vol 5, pp. 01-12· 0 citations
TL;DR
This paper provides a comprehensive survey of the intersection of FL, Blockchain, and Edge Computing, analyzing key opportunities, current solutions, and open challenges and discusses architectural frameworks, real-world applications, and future research directions.
Abstract
The convergence of Federated Learning (FL), Blockchain, and Edge Computing presents a transformative paradigm for decentralized, secure, and privacy-preserving machine learning at the network edge. FL enables collaborative model training without centralizing data, while Blockchain provides immutable and transparent mechanisms for trust, accountability, and coordination among distributed edge nodes. Edge computing further enhances this ecosystem by offering low-latency computation near data sources. Despite the promise of this triad, significant challenges persist in terms of scalability, energy efficiency, consensus mechanisms, data and model security, and system heterogeneity. This paper provides a comprehensive survey of the intersection of FL, Blockchain, and Edge Computing, analyzing key opportunities, current solutions, and open challenges. We also discuss architectural frameworks, real-world applications, and future research directions.
In recent years, the rapid growth of distributed artificial intelligence (AI) and blockchain technology has led to new opportunities for building secure, transparent, and privacy-preserving learning systems. Federated Learning (FL) enables multiple users or organizations to collaboratively train a global AI model without sharing their private data, while Blockchain provides immutability, traceability, and decentralized trust. However, most existing blockchain-based FL systems are limited to a single network, lacking interoperability and scalability across multiple chains. This review paper explores the emerging concept of Cross-Chain Federated Learning (CCFL), which integrates federated learning with cross-chain blockchain communication to achieve secure and interoperable decentralized AI. The paper discusses existing research works, current architectures, algorithms, and cross-chain mechanisms, identifying key challenges such as model verification, communication overhead, and data integrity. Furthermore, it highlights how the proposed framework addresses these challenges by using smart contracts, cryptographic hashing, and relayer-based synchronization.The study concludes that integrating FL with cross-chain blockchain technology can significantly enhance privacy, security, and collaboration among diverse AI systems, paving the way for next-generation decentralized intelligence.
Keywords— Federated Learning, Blockchain, Cross-Chain Communication, Decentralized AI, Data Privacy, Smart Contracts, Secure Aggregation, Interoperability
Vikrant Thombare, Mahendra Sawane· International Journal of Cre...· 0 citations
Federated Learning (FL) enables distributed machine learning without sharing raw data, but its reliance on a central aggregation server introduces critical vulnerabilities gradient inversion attacks, Byzantine poisoning, free-riding by rational participants, and single-point-of-failure risk. Blockchain has been proposed as a structural remedy, giving rise to the field of Blockchain-Enabled Federated Learning (BEFL). reviews exactly eight representative peer-reviewed BEFL systems published selected to cover four core challenge dimensions: privacy, security, scalability, and incentive design. compare each system across accuracy under data heterogeneity, formal privacy guarantees, Byzantine robustness, throughput, and communication efficiency. find that every reviewed system excels on one or two dimensions while underperforming on others, and that no single published system simultaneously resolves all four challenges. Based on this review identify four fundamental research gaps and conclude with a structured research agenda. This study also reveals that the base FL optimizer contributes more to model accuracy than any blockchain or privacy mechanism a finding with significant design implications.
Raman Dubey, A. Jain, Richa Sharma· International Conference on...· 0 citations
The growth of the Internet of Things (IoT) has introduced significant security challenges, mainly due to the resource constraints of devices and the limitations of centralized architectures. This paper proposes a blockchain-based Zero-Trust framework for secure and scalable IoT systems. The approach is architecture-agnostic and combines decentralized identity management, hybrid data storage, and edge-assisted computation. To optimize resource usage, raw data are stored off-chain while cryptographic hashes are anchored on the blockchain, ensuring integrity and immutability. A Merkle tree structure is employed to aggregate data efficiently, reducing communication overhead and blockchain transaction costs. Experimental results demonstrate that lightweight cryptographic mechanisms, combined with Merkle-based aggregation, provide strong security guarantees with low energy consumption. The proposed framework achieves improved scalability, robustness, and efficiency, making it suitable for resource-constrained IoT environments.
Florian Bonelli, Alexandre dos Santos Roque, E. P. de Freitas· International Conference on...· 0 citations
The Quantum-Blockchain Fusion Framework (QBFF) introduces a
pioneering solution for dynamic access control in federated grid computing
systems. Addressing critical challenges in security, scalability, and
adaptability, QBFF combines quantum key distribution (QKD) for secure
communication with blockchain technology for decentralized identity
management and policy enforcement. The framework employs a hybrid
consensus mechanism, integrating proof-of-authority and quantum
randomness to enhance efficiency while maintaining robust security.
Experimental results demonstrate significant improvements in transaction
throughput, reduced latency, and resilience against cyber threats like
identity spoofing and consensus manipulation. QBFF also supports dynamic
policy updates with real-time adaptability, enabling scalable and efficient
operations in distributed environments. This research establishes QBFF as a
comprehensive solution for modern grid systems, ensuring security and
scalability in the face of evolving technological demands.
A. R. Johnson Durai· International Journal of Mod...· 0 citations
Overall, this review demonstrates that blockchain-based cybersecurity frameworks provide a secure, transparent, and resilient foundation for protecting smart digital environments against increasingly sophisticated cyber threats while supporting trustworthy and scalable digital transformation.
M. Kayla, Crispinus Ode, Marion Sanaipei· The Eastasouth Journal of In...· 0 citations
In recent years, fog computing has emerged as a pivotal paradigm to overcome the limitations of traditional cloud computing by decentralizing data processing and bringing it closer to the data source. This shift, however, introduces significant security challenges, such as data integrity, authentication, and secure communication among distributed fog nodes. Traditional security measures often fall short in addressing these challenges due to the dynamic and decentralized nature of fog computing environments. Blockchain technology, with its decentralized, immutable, and transparent ledger system, presents a promising solution to these security issues. This research investigates the integration of blockchain technology into fog computing systems to enhance their security framework. By leveraging the consensus algorithms and cryptographic techniques inherent in blockchain, the study aims to ensure data integrity, prevent unauthorized access, and secure communications within fog computing environments. The research utilizes iFogSim, a simulation toolkit for modeling and simulating fog computing environments, to implement and evaluate the proposed blockchain-based security framework. The primary objectives of this study include assessing the current security vulnerabilities in fog computing, developing a customized blockchain-based security framework, and rigorously evaluating its performance through empirical analysis and simulation. The findings are expected to provide actionable insights and recommendations for deploying secure and efficient fog computing architectures, thereby contributing to the broader adoption of blockchain-enhanced fog computing systems in various sectors such as IoT, healthcare, and smart cities. This study not only addresses the theoretical underpinnings of blockchain integration in fog computing but also bridges the gap between theory and practical implementation, aiming to advance the field of secure distributed computing in the digital age. In this article, iFogSim will be used to simulate the integration of blockchain and fog computing.
Choon Keat Low, Fung Lim, Bee-Sian Tan et al.· IEM Journal· 0 citations
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