Scaling Federated Learning Solutions with Kubernetes for Synthesizing Histopathology Images
Journal:
arXiv
Published Date:
Apr 5, 2025
Abstract
In the field of deep learning, large architectures often obtain the best
performance for many tasks, but also require massive datasets. In the
histological domain, tissue images are expensive to obtain and constitute
sensitive medical information, raising concerns about data scarcity and
privacy. Vision Transformers are state-of-the-art computer vision models that
have proven helpful in many tasks, including image classification. In this
work, we combine vision Transformers with generative adversarial networks to
generate histopathological images related to colorectal cancer and test their
quality by augmenting a training dataset, leading to improved classification
accuracy. Then, we replicate this performance using the federated learning
technique and a realistic Kubernetes setup with multiple nodes, simulating a
scenario where the training dataset is split among several hospitals unable to
share their information directly due to privacy concerns.
