# Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning

arXiv: https://arxiv.org/abs/2110.00394 · HTML: https://mlsanity.com/paper/2110.00394/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 19 of 19)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Training behavior of deep neural network in frequency domain](https://mlsanity.com/paper/1807.01251/) (2018)
- [Federated Mutual Learning](https://mlsanity.com/paper/2006.16765/) (2020)
- [Federated Learning for Breast Density Classification: A Real-World Implementation](https://mlsanity.com/paper/2009.01871/) (2020)
- [Siloed Federated Learning for Multi-Centric Histopathology Datasets](https://mlsanity.com/paper/2008.07424/) (2020)
- [Inverse Distance Aggregation for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2008.07665/) (2020)
- [Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach](https://mlsanity.com/paper/1912.11027/) (2019)
- [Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification](https://mlsanity.com/paper/2004.09694/) (2020)
- [CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis](https://mlsanity.com/paper/2008.00119/) (2020)

## Built on by (most cited first, 25 of 25)

- [DENSE: Data-Free One-Shot Federated Learning](https://mlsanity.com/paper/2112.12371/) (2021)
- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation](https://mlsanity.com/paper/2308.15367/) (2023)
- [Personalizing Federated Medical Image Segmentation via Local Calibration](https://mlsanity.com/paper/2207.04655/) (2022)
- [Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts](https://mlsanity.com/paper/2312.02567/) (2023)
- [Privacy Preserving Prompt Engineering: A Survey](https://mlsanity.com/paper/2404.06001/) (2024)
- [Federated Generative Learning with Foundation Models](https://mlsanity.com/paper/2306.16064/) (2023)
- [IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation](https://mlsanity.com/paper/2204.08467/) (2022)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Delving into the Adversarial Robustness of Federated Learning](https://mlsanity.com/paper/2302.09479/) (2023)
- [Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging](https://mlsanity.com/paper/2407.02356/) (2024)
- [Suppressing Poisoning Attacks on Federated Learning for Medical Imaging](https://mlsanity.com/paper/2207.10804/) (2022)
- [Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation](https://mlsanity.com/paper/2306.03511/) (2023)
- [CXR-FL: Deep Learning-Based Chest X-ray Image Analysis Using Federated Learning](https://mlsanity.com/paper/2204.05203/) (2022)
- [FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning](https://mlsanity.com/paper/2311.18559/) (2023)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring](https://mlsanity.com/paper/2305.00738/) (2023)
- [DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation](https://mlsanity.com/paper/2410.12926/) (2024)
- [Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation](https://mlsanity.com/paper/2310.15371/) (2023)
- [FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learning](https://mlsanity.com/paper/2502.16832/) (2025)
- [FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning](https://mlsanity.com/paper/2302.13571/) (2023)
- [Non-parametric regularization for class imbalance federated medical image classification](https://mlsanity.com/paper/2407.12446/) (2024)
- [Federated Active Learning Under Extreme Non-IID and Global Class Imbalance](https://mlsanity.com/paper/2603.10341/) (2026)
- [FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentation](https://mlsanity.com/paper/2603.11659/) (2026)
- [Personalized Federated Learning with Residual Fisher Information for Medical Image Segmentation](https://mlsanity.com/paper/2603.14848/) (2026)

## Abstract (arXiv)

Nowadays, deep learning methods with large-scale datasets can produce clinically useful models for computer-aided diagnosis. However, the privacy and ethical concerns are increasingly critical, which make it difficult to collect large quantities of data from multiple institutions. Federated Learning (FL) provides a promising decentralized solution to train model collaboratively by exchanging client models instead of private data. However, the server aggregation of existing FL methods is observed to degrade the model performance in real-world medical FL setting, which is termed as retrogress. To address this problem, we propose a personalized retrogress-resilient framework to produce a superior personalized model for each client. Specifically, we devise a Progressive Fourier Aggregation (PFA) at the server to achieve more stable and effective global knowledge gathering by integrating client models from low-frequency to high-frequency gradually. Moreover, with an introduced deputy model to receive the aggregated server model, we design a Deputy-Enhanced Transfer (DET) strategy at the client and conduct three steps of Recover-Exchange-Sublimate to ameliorate the personalized local model by transferring the global knowledge smoothly. Extensive experiments on real-world dermoscopic FL dataset prove that our personalized retrogress-resilient framework outperforms state-of-the-art FL methods, as well as the generalization on an out-of-distribution cohort. The code and dataset are available at https://github.com/CityU-AIM-Group/PRR-FL.