Privacy-Preserving Federated Learning: Challenges, Techniques, and Prospects for Distributed AI

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Review highlights challenges and techniques of federated learning for privacy in IoT and healthcare applications.

Federated learning improves privacy by enabling decentralized training without sharing raw data.
Key techniques include differential privacy and secure aggregation to enhance data security during model updates.
Challenges like data heterogeneity and regulatory compliance hinder the adoption and effectiveness of federated learning.
Future directions involve improving communication efficiency and integrating federated learning with IoT and edge environments.

Kumar et al. (2025) studied this question.

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View Full Paper

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Authors

Review highlights challenges and techniques of federated learning for privacy in IoT and healthcare applications.

Federated learning improves privacy by enabling decentralized training without sharing raw data.
Key techniques include differential privacy and secure aggregation to enhance data security during model updates.
Challenges like data heterogeneity and regulatory compliance hinder the adoption and effectiveness of federated learning.
Future directions involve improving communication efficiency and integrating federated learning with IoT and edge environments.

Kumar et al. (2025) studied this question.

View Full Paper

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