@article {16776,
	title = {Exploring the Synergy of Fog Computing, Blockchain, and Federated Learning for IoT Applications: A Systematic Literature Review},
	journal = {IEEE Access},
	volume = {vol 12},
	year = {2024},
	abstract = {The proliferation of Internet of Things (IoT) applications poses formidable challenges in managing data processing, privacy, and security. In response, technologies such as Fog Computing (FC), Blockchain (BC), and Federated Learning (FL) have emerged as promising solutions. Combining these technologies can broaden their scope, and impose novel challenges. This paper conducts a Systematic Literature Review (SLR) to investigate their integration within the IoT domain, systematically evaluating the current state-of-the-art by analyzing 40 papers against 38 extraction criteria, encompassing technical characteristics specific to FC, BC, FL, or their integration. The findings offer insights into the advantages, challenges, opportunities, and limitations of this integration, addressing data processing, privacy, and security concerns in IoT. By filling a research gap and directly examining FC, BC, and FL interoperability across architectural layers, this study contributes to knowledge expansion in the field. This paper proposes a novel framework for implementing FL and BC within FC environments for IoT applications, alongside a comprehensive synthesis of existing literature, distinguishing it from previous research efforts. Furthermore, it offers valuable insights into the current landscape, identifies research needs, and proposes future research directions. The framework and literature synthesis provided allow readers to access customized information on FC-BC-FL integration, aiding in designing and implementing robust IoT solutions.},
	keywords = {Blockchain, edge computing, federated learning, fog computing, Internet of Things, systematic literature review},
	doi = {10.1109/ACCESS.2024.3398034},
	url = {https://ieeexplore.ieee.org/document/10522660},
	author = {Wilson Valdez Solis and Juan Marcelo Parra-Ullauri and Attila Kert{\'e}sz}
}
@article {16780,
	title = {PriFoB: A Privacy-aware Fog-enhanced Blockchain-based system for Global Accreditation and Credential Verification},
	journal = {Journal of Network and Computer Applications},
	volume = {205},
	year = {2022},
	abstract = {Trusted online credential management solutions are needed for instant and practical verification. Most of the available frameworks targeting this field violate the privacy of end-users or lack sufficient solutions in terms of security and Quality-of-Service (QoS). In this paper, we propose a Privacy-aware Fog-enhanced Blockchain-based online credential management solution, namely PriFoB. Our proposed solution adopts a public permissioned Blockchain model with different reliable encryption schemes, standardized Zero-Knowledge-Proofs (ZKPs) and Digital Signatures (DSs) within a Fog{\textendash}Blockchain integrated framework, which is also GDPR compliant. We deploy both the Proof-of-Authority (PoA) and the Signatures-of-Work (SoW) consensus algorithms for efficient and secure handling of Verifiable Credentials (VCs) and global accreditation of VC issuers, respectively. Furthermore, we propose a novel three-dimensional DAG-based model of the Distributed Ledger (3DDL), and provide a ready-to-deploy PriFoB implementation. We discuss insights regarding the utilization and the potential of PriFoB, and evaluate it in terms of security, privacy, latency, throughput and power utilization. We analyze its performance in different layers of a Fog-enabled cloud architecture with simulation and emulation, and we show that PriFoB outperforms several Blockchain-based solutions utilizing Ethereum, Hyperledger Fabric, Hyperledger Besu and Hyperledger Indy platforms.},
	keywords = {Blockchain, Credential Verification, fog computing, Global accreditation, Privacy, Self Sovereign Identity (SSI)},
	issn = {1084-8045},
	doi = {https://doi.org/10.1016/j.jnca.2022.103440},
	url = {https://www.sciencedirect.com/science/article/pii/S1084804522000935},
	author = {Hamza Baniata and Attila Kert{\'e}sz}
}
