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Software Bug Detection using Federated Learning Models: A Comparative Study
This research aims to advance the field of federated learning by addressing privacy, domain shifts, and personalization challenges, particularly in the context of mobile and digital healthcare. By developing robust and scalable solutions, the proposed framework has the potential to significantly enhance patient care, diagnostics, and personalized treatment while maintaining stringent privacy standards
Faculty Supervisor:
Banani Roy
Student:
Partner:
Università della Svizzera italiana
Discipline:
Computer science
Sector:
Artificial Intelligence; Information and Communications Technology; Health and Related Sciences & Technology
University:
University of Saskatchewan
Program:
Globalink Research Award
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