# Scaling Federated Learning Solutions with Kubernetes for Synthesizing Histopathology Images Source: [Scaling Federated Learning Solutions with Kubernetes for Synthesizing Histopathology Images]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:15.407Z Authors: Andrei-Alexandru Preda, Iulian-Marius T\\u{a}iatu, Dumitru-Clementin Cercel Published: 2025-04-05T10:32:56.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()