Federated Learning Implementation for Privacy-Preserving AI Model Training Across Distributed Cloud Platforms
Keywords:
Federated Learning, Privacy-Preserving AI, Distributed Cloud, Differential Privacy, Secure Aggregation, Edge Computing, Model TrainingAbstract
In response to increasing privacy concerns and the demand for scalable, decentralized machine learning (ML) models, federated learning (FL) has emerged as a promising paradigm. This paper explores the implementation of federated learning across distributed cloud platforms to enable privacy-preserving AI model training without centralized data storage. We present an architecture integrating secure model aggregation, differential privacy, and platform interoperability. Additionally, we review literature to situate current developments, analyze system performance, and highlight security and scalability trade-offs. Our experimental simulations demonstrate that federated learning offers significant privacy guarantees while maintaining competitive model accuracy compared to centralized training.
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Copyright (c) 2024 Zhang Kin Linine (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
