July 14, 2025

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

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Authors

AWAhsan Wajahat KZKailong ZhangZhejiang Sci-Tech University JLJahanzaib LatifNorthwestern Polytechnical University

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Implication

Comprehensive review highlights advancements and challenges in federated learning, suggesting future developments for enhanced data privacy and model performance.

Key Points

Federated learning enhances data privacy while enabling collaborative model training across devices, fostering significant innovation.
Key algorithmic approaches like Federated Averaging (FedAvg) address issues such as data heterogeneity and client drift during model training.
The review examines privacy-preserving technologies like differential privacy and homomorphic encryption, weighing their impact on model performance.
Future directions include personalized techniques and blockchain frameworks to bolster security and compliance in federated learning systems.

Cite This Study

Wajahat et al. (2025) studied this question.

synapsesocial.com/papers/68af76b97567bf4f94fefc1c https://doi.org/10.64680/jisads.v3i1.38

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Implication

Comprehensive review highlights advancements and challenges in federated learning, suggesting future developments for enhanced data privacy and model performance.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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