Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A. and Smith, V. (2020) Federated Optimization in Heterogeneous Networks. Proceedings of Machine Learning and Systems.
https://proceedings.mlsys.org/paper_files/paper/2020/file/1f5fe83998a09396ebe6477d9475ba0c-Paper.pdf

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.