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Kalapaaking, A.P., Khalil, I., Rahman, M.S., Atiquzzaman, M., Yi, X. and Almashor, M. (2023) Blockchain-Based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things. IEEE Transactions on Industrial Informatics, 19, 1703-1714.
https://doi.org/10.1109/tii.2022.3170348

has been cited by the following article:

• TITLE: Beyond the Cloud: Federated Learning and Edge AI for the Next Decade

  AUTHORS: Sooraj George Thomas, Praveen Kumar Myakala

  KEYWORDS: Federated Learning, Edge AI, Decentralized Computing, Privacy-Preserving AI, Blockchain, Quantum AI

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.2, February 20, 2025

  ABSTRACT: As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.