What question did this study set out to answer?

To explore the architectures, challenges, and future research avenues of federated learning.

April 7, 2026Open Access

Federated Learning: A Systematic Review of Architecture, Challenges and Research Directions

View Full Paper

Ask AI

Bookmark

Authors

This review analyzes federated learning challenges and research directions, highlighting privacy concerns and deployment issues.

To explore the architectures, challenges, and future research avenues of federated learning.
Reviewed twelve peer-reviewed surveys and papers on federated learning from 2017 to 2025.
Analyzed federated learning architectures, communication mechanisms, and privacy-preserving techniques.
Identified challenges including non-IID data, communication costs, and adversarial threats.
Federated learning faces significant technical challenges like non-IID data distributions and high communication costs.
Emerging research areas identified include lightweight optimization and blockchain-based trust mechanisms.
The review provides a comprehensive summary of existing literature and outlines key open problems for future research.

Rathod et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark