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

Area of Science:

• Computer Science
• Machine Learning
• Distributed Systems

Background:

• Federated learning (FL) is a distributed machine learning paradigm.
• It enables collaborative training of a shared model across multiple decentralized nodes.
• FL ensures data privacy by keeping raw data localized and only sharing model updates.

Purpose of the Study:

• To provide a comprehensive review of federated learning.
• To cover its principles, strategies, applications, and tools.
• To identify opportunities, challenges, and future research directions in FL.

Main Methods:

• Literature review of federated learning principles and strategies.
• Analysis of existing federated learning applications and tools.
• Discussion of challenges and future research avenues.

Main Results:

• Federated learning significantly enhances data privacy and security.
• Key advantages include scalability and efficiency in distributed environments.
• FL strategies are particularly beneficial for high-risk applications requiring confidentiality.

Conclusions:

• Federated learning is a powerful approach for privacy-preserving machine learning.
• It addresses critical data confidentiality concerns in sensitive domains.
• Further research is needed to explore its full potential and overcome existing challenges.