Unifying Intelligence: Federated Learning in Cloud Environments for Decentralized Machine Learning
Abstract: The rapid growth of data generation and the increasing demand for machine learning models have given rise to novel approaches in the realm of distributed computing. Federated Learning, as a paradigm, allows machine learning models to be trained across decentralized data sources, paving the way for enhanced privacy, efficiency, and scalability. This rsearch-oriented descriptive article explores the implementation of Federated Learning techniques in cloud environments, unraveling the intricacies of decentralized model training, addressing challenges, and examining real-world applications. Keywords, relevant studies, and references are provided to offer a comprehensive resource for researchers and practitioners in the field.
Keywords: Federated Learning, Cloud Computing, Decentralized Model Training, Privacy-Preserving Techniques, Machine Learning, Edge Computing, Data Privacy, Communication Overhead, Security,, Real-world Applications, Case Studies, Observational Studies
How to Cite?: Dr. Angajala Srinivasa Rao, "Unifying Intelligence: Federated Learning in Cloud Environments for Decentralized Machine Learning", Volume 12 Issue 12, December 2023, International Journal of Science and Research (IJSR), Pages: 997-999, https://www.ijsr.net/getabstract.php?paperid=SR231212134726, DOI: https://dx.doi.org/10.21275/SR231212134726