Personalized-Federated-Learning-for-Privacy--Preserving-and-Scalable-IoT-Driven-Smart-Healthcare
A Personalized Federated Learning (PFL-HCare) framework for IoT healthcare. Features MAML meta-learning, Differential Privacy (RDP), and gradient q...
Updated means this listing was last refreshed on Apr 26, 2026.
About Personalized-Federated-Learning-for-Privacy--Preserving-and-Scalable-IoT-Driven-Smart-Healthcare
Pricing:Freemium
A Personalized Federated Learning (PFL-HCare) framework for IoT healthcare. Features MAML meta-learning, Differential Privacy (RDP), and gradient quantization for efficiency. Includes a React/FastAPI dashboard for real-time monitoring. Tisha-runwal/Personalized-Federated-Learning-for-Privacy--Preserving-and-Scalable-IoT-Driven-Smart-Healthcare is an open source project on GitHub with 268 stars. Built primarily in Python. Licensed under MIT. Topics: differential-privacy, fastapi, federated-learning, iot-healthcare, machine-learning, maml, meta-learning, privacy-preserving, pytorch, react.
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