Multi-Cloud AI Model Training Using Federated Learning and Secure Aggregation Techniques
Keywords:
Multi-Cloud AI, Federated Learning, Secure Aggregation, Data Privacy, Distributed Machine Learning, Cloud Computing, Model TrainingAbstract
With the increasing adoption of cloud computing for artificial intelligence (AI) model training, enterprises are shifting towards multi-cloud architectures to leverage cost efficiency, flexibility, and enhanced security. Federated Learning (FL) has emerged as a promising technique to train AI models across multiple distributed environments while preserving data privacy. Secure aggregation techniques further ensure data confidentiality by allowing model updates to be securely combined. This paper explores the implementation of FL in multi-cloud environments, examines secure aggregation methods, and presents their benefits and challenges. We provide a comparative analysis of federated learning models in multi-cloud settings and highlight future research directions.
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Copyright (c) 2024 Kalevi Tikkanen (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
