Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging

HIGHLIGHTS

• What: This study explores the challenges posed by data heterogeneity on FL algorithms using the COVIDx CXR-3 dataset as a case study. As each client trains the model locally, its local objectives might diverge significantly from the collective goal. The research provides key insights into the challenges and intricacies of federated learning in the context of data heterogeneity. The authors provide a comparative analysis of the IID and non-IID environments using the proposed approach.
• Who: Muhammad Babar and colleagues from the Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, Saudi Arabia . . .
  

   

  

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