Federated Learning in Cloud Environments to Protect Data Privacy
Keywords:
Federated Learning, Cloud Environments, Data Privacy, Distributed Machine Learning, Privacy-Preserving Techniques, Secure Aggregation, Differential Privacy, Homomorphic Encryption, Collaborative Learning.Abstract
In dispersed cloud environments, federated learning (FL) has become a viable method for training machine learning models while protecting data privacy. FL reduces privacy hazards by enabling numerous users to work together to train models without exchanging sensitive data, in contrast to standard centralized learning techniques. The use of FL in cloud-based infrastructures to protect data privacy in a variety of sectors, including as healthcare, finance, and the Internet of Things, is examined in this study. FL improves security and lessens the need for data transfer by aggregating local model updates and decentralizing model training. We examine important privacy-preserving methods in FL, including safe aggregation, homomorphic encryption, and differential privacy, and evaluate how they affect model accuracy, scalability, and performance. We also go over the difficulties of putting FL into practice in actual cloud contexts, such handling resource limitations, consistency issues, and heterogeneous data. In order to maintain strong data privacy and promote confidence in cooperative machine learning systems, we conclude by suggesting potential paths for developing federated learning models in cloud ecosystems.
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