July 14, 2025

A Comprehensive Review of Federated Learning: Advancements, Challenges, and Future Directions

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This review details federated learning's evolution, revealing advantages in data privacy and highlighting persistent challenges.

Federated learning enhances data privacy while reducing communication overhead and improving model collaboration, which is essential in various domains.
Key algorithmic advancements, including federated averaging and its variants, address issues like data heterogeneity and client drift, allowing improved model accuracy.
The review examines pressing challenges such as communication bottlenecks and security vulnerabilities in federated learning frameworks, emphasizing areas needing resolution.
Future research directions suggest adaptive personalization and blockchain-assisted security frameworks, advocating for standardized approaches in implementing federated learning.

Wajahat et al. (2025) studied this question.

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