# Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition

arXiv: https://arxiv.org/abs/2409.19934 · HTML: https://mlsanity.com/paper/2409.19934/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Using Pre-Training Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1901.09960/) (2019)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [Privacy and Robustness in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/2012.06337/) (2020)
- [Surgical Data Science -- from Concepts toward Clinical Translation](https://mlsanity.com/paper/2011.02284/) (2020)
- [Learning to Detect Malicious Clients for Robust Federated Learning](https://mlsanity.com/paper/2002.00211/) (2020)
- [A multi-centre polyp detection and segmentation dataset for generalisability assessment](https://mlsanity.com/paper/2106.04463/) (2021)
- [A Survey on the Robustness of Computer Vision Models against Common Corruptions](https://mlsanity.com/paper/2305.06024/) (2023)
- [Assessing deep learning methods for the identification of kidney stones in endoscopic images](https://mlsanity.com/paper/2103.01146/) (2021)
- [Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning](https://mlsanity.com/paper/2210.13654/) (2022)
- [Transferring Adversarial Robustness Through Robust Representation Matching](https://mlsanity.com/paper/2202.09994/) (2022)
- [On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space](https://mlsanity.com/paper/2302.12351/) (2023)

## Built on by (most cited first, 2 of 2)

- [FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopy](https://mlsanity.com/paper/2603.19512/) (2026)
- [Federated Learning Framework for Privacy-Preserving Kidney Stone Detection](https://mlsanity.com/paper/2609.19740/) (2026)

## Abstract (arXiv)

Deep learning developments have improved medical imaging diagnoses dramatically, increasing accuracy in several domains. Nonetheless, obstacles continue to exist because of the requirement for huge datasets and legal limitations on data exchange. A solution is provided by Federated Learning (FL), which permits decentralized model training while maintaining data privacy. However, FL models are susceptible to data corruption, which may result in performance degradation. Using pre-trained models, this research suggests a strong FL framework to improve kidney stone diagnosis. Two different kidney stone datasets, each with six different categories of images, are used in our experimental setting. Our method involves two stages: Learning Parameter Optimization (LPO) and Federated Robustness Validation (FRV). We achieved a peak accuracy of 84.1% with seven epochs and 10 rounds during LPO stage, and 77.2% during FRV stage, showing enhanced diagnostic accuracy and robustness against image corruption. This highlights the potential of merging pre-trained models with FL to address privacy and performance concerns in medical diagnostics, and guarantees improved patient care and enhanced trust in FL-based medical systems.