Federated Learning: A Privacy-Preserving Approach to Distributed Machine Learning
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Abstract
Federated learning is an emerging paradigm that allows distributed training of machine learning models while preserving data privacy. This paper explores the principles and challenges of federated learning, focusing on its applications in industries where data privacy is paramount, such as healthcare and finance. We present case studies demonstrating how federated learning can achieve accurate models without compromising user privacy.
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