Novel Federated Learning Approaches for Privacy-Preserving Machine Learning: A Comparative Evaluation
Abstract
Federated learning enables AI model training across decentralized data sources without exposing sensitive information. This paper introduces a novel federated learning strategy that enhances privacy preservation while maintaining high model performance. We compare traditional centralized learning methods with state-of-the-art federated learning techniques across multiple datasets. The study reveals trade-offs in accuracy, security, and computational overhead, providing valuable insights for deploying federated AI models in healthcare, finance, and IoT environments.
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