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SAFE-MED for privacy-preserving federated learning in IoMT via adversarial neural cryptography
Federated learning (FL) offers a promising paradigm for distributed model training in Internet of Medical Things (IoMT) systems, where patient data privacy and device heterogeneity are critical concerns. However, conventional FL remains vulnerable to gradient leakage, model poisoning, and adversaria
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