Federated Learning in Edge Computing: Challenges, Security, and Future Directions
Abstract
Federated Learning (FL) has emerged as a promising paradigm for training ML models across distributed edge devices while preserving user privacy. This review paper provides an in-depth analysis of FL architectures, optimization techniques, and security challenges, including adversarial attacks, model poisoning, and data heterogeneity. We explore real-world applications in IoT, healthcare, and smart cities and discuss future advancements to improve scalability, efficiency, and robustness in FL frameworks.
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