Myakala, P.K., Bura, C. and Jonnalagadda, A.K. (2024) Federated Learning and Data Privacy: A Review of Challenges and Opportunities. International Journal of Research Publication and Reviews, 5, 1867-1879.
https://doi.org/10.55248/gengpi.5.1224.3512

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.