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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy
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Project title: Large ensembles of machine learning forecasts for advanced nonlinear filters in atmospheric data assimilation Supervisors: Sarah L Dance (UoR & NCEO), Eviatar Bach (UoR) & Amos
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of dehydration using a low-power radio-frequency (RF) sensor. The research objectives include design optimization to improve wearability, robust data acquisition using machine learning and establishing correlation
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment