# Scaling Federated Learning Solutions with Kubernetes for Synthesizing Histopathology Images

arXiv: https://arxiv.org/abs/2504.04130 · HTML: https://mlsanity.com/paper/2504.04130/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 5 Apr 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://mlsanity.com/paper/2104.01136/) (2021)
- [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://mlsanity.com/paper/1706.08500/) (2017)
- [MedViT: A Robust Vision Transformer for Generalized Medical Image Classification](https://mlsanity.com/paper/2302.09462/) (2023)
- [ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis](https://mlsanity.com/paper/2304.01053/) (2023)
- [UPB at SemEval-2020 Task 8: Joint Textual and Visual Modeling in a Multi-Task Learning Architecture for Memotion Analysis](https://mlsanity.com/paper/2009.02779/) (2020)
- [LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on color](https://mlsanity.com/paper/1810.10395/) (2018)
- [UPB at SemEval-2020 Task 12: Multilingual Offensive Language Detection on Social Media by Fine-tuning a Variety of BERT-based Models](https://mlsanity.com/paper/2010.13609/) (2020)
- [UPB at SemEval-2020 Task 11: Propaganda Detection with Domain-Specific Trained BERT](https://mlsanity.com/paper/2009.05289/) (2020)
- [Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis](https://mlsanity.com/paper/2306.07845/) (2023)

## Abstract (arXiv)

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.