Federated learning: Overview, strategies, applications, tools and future directions

Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications. •In-depth Review and Categorization of Federated Learning Strategies.•Examination of Security Concerns in Federated Learning.•Applications of Federated Learning Across Various Domains.•Insight into Federated Learning-based Frameworks and Tools.•Future Directions and Emerging Opportunities in Federated Learning.