%0 Journal Article
%T Beyond the Cloud: Federated Learning and Edge AI for the Next Decade
%A Sooraj George Thomas
%A Praveen Kumar Myakala
%J Journal of Computer and Communications
%P 37-50
%@ 2327-5227
%D 2025
%I Scientific Research Publishing
%R 10.4236/jcc.2025.132004
%X 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.
%K Federated Learning
%K Edge AI
%K Decentralized Computing
%K Privacy-Preserving AI
%K Blockchain
%K Quantum AI
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=140719