  Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable
advancements, particularly in healthcare. Within medical imaging, ML models
hold the promise of improving disease diagnoses, treatment planning, and
post-treatment monitoring. Various computer vision tasks like image
classification, object detection, and image segmentation are poised to become
routine in clinical analysis. However, privacy concerns surrounding patient
data hinder the assembly of large training datasets needed for developing and
training accurate, robust, and generalizable models. Federated Learning (FL)
emerges as a compelling solution, enabling organizations to collaborate on ML
model training by sharing model training information (gradients) rather than
data (e.g., medical images). FL's distributed learning framework facilitates
inter-institutional collaboration while preserving patient privacy. However,
FL, while robust in privacy preservation, faces several challenges. Sensitive
information can still be gleaned from shared gradients that are passed on
between organizations during model training. Additionally, in medical imaging,
quantifying model confidence\uncertainty accurately is crucial due to the noise
and artifacts present in the data. Uncertainty estimation in FL encounters
unique hurdles due to data heterogeneity across organizations. This paper
offers a comprehensive review of FL, privacy preservation, and uncertainty
estimation, with a focus on medical imaging. Alongside a survey of current
research, we identify gaps in the field and suggest future directions for FL
research to enhance privacy and address noisy medical imaging data challenges.
