Federated Learning: Enhancing Privacy -Preserving Machine Learning Across Diverse Applications

Federated Learning (FL) has emerged as a powerful paradigm that allows machine learning models to be trained across decentralized devices while maintaining data privacy. This paper explores the advancements in Federated Learning, discussing its underlying principles, key challenges, and applications. The paper highlights the role of federated optimization algorithms, privacy preservation mechanisms, and communication efficiency improvements that enable FL to be applied in real-world scenarios. We also examine the impact of data heterogeneity, security concerns, and model convergence in federated environments. Finally, we present potential future directions for Federated Learning, emphasizing its integration with emerging technologies like edge computing and ۵G.

کلیدواژه ها:

Federated Learning ، Decentralized Machine Learning ، Privacy ، Communication Efficiency ، Model Convergence ، Edge Computing ، ۵G