# FLOP: Federated Learning on Medical Datasets using Partial Networks

arXiv: https://arxiv.org/abs/2102.05218 · HTML: https://mlsanity.com/paper/2102.05218/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 46 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [iDLG: Improved Deep Leakage from Gradients](https://mlsanity.com/paper/2001.02610/) (2020)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [COVID-19 Image Data Collection](https://mlsanity.com/paper/2003.11597/) (2020)
- [Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging](https://mlsanity.com/paper/2007.06537/) (2020)
- [Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data](https://mlsanity.com/paper/1810.08553/) (2018)
- [Decoupled Parallel Backpropagation with Convergence Guarantee](https://mlsanity.com/paper/1804.10574/) (2018)
- [Experiments of Federated Learning for COVID-19 Chest X-ray Images](https://mlsanity.com/paper/2007.05592/) (2020)
- [Graph-Driven Generative Models for Heterogeneous Multi-Task Learning](https://mlsanity.com/paper/1911.08709/) (2019)
- [Ouroboros: On Accelerating Training of Transformer-Based Language Models](https://mlsanity.com/paper/1909.06695/) (2019)

## Built on by (most cited first, 30 of 42)

- [Towards Building the Federated GPT: Federated Instruction Tuning](https://mlsanity.com/paper/2305.05644/) (2023)
- [Federated Class-Incremental Learning](https://mlsanity.com/paper/2203.11473/) (2022)
- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [Decentralized Federated Learning through Proxy Model Sharing](https://mlsanity.com/paper/2111.11343/) (2021)
- [pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning](https://mlsanity.com/paper/2206.03655/) (2022)
- [Efficient Personalized Federated Learning via Sparse Model-Adaptation](https://mlsanity.com/paper/2305.02776/) (2023)
- [Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction](https://mlsanity.com/paper/2209.15245/) (2022)
- [FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction](https://mlsanity.com/paper/2112.10166/) (2021)
- [Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning](https://mlsanity.com/paper/2309.01289/) (2023)
- [Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks](https://mlsanity.com/paper/2302.05675/) (2023)
- [Federated Learning from Small Datasets](https://mlsanity.com/paper/2110.03469/) (2021)
- [An Efficient Federated Distillation Learning System for Multi-task Time Series Classification](https://mlsanity.com/paper/2201.00011/) (2021)
- [A Survey on Class Imbalance in Federated Learning](https://mlsanity.com/paper/2303.11673/) (2023)
- [Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination](https://mlsanity.com/paper/2305.10730/) (2023)
- [Compositional federated learning: Applications in distributionally robust averaging and meta learning](https://mlsanity.com/paper/2106.11264/) (2021)
- [Recent Methodological Advances in Federated Learning for Healthcare](https://mlsanity.com/paper/2310.02874/) (2023)
- [Co-clustering for Federated Recommender System](https://mlsanity.com/paper/2411.01690/) (2024)
- [Advancements in Federated Learning: Models, Methods, and Privacy](https://mlsanity.com/paper/2302.11466/) (2023)
- [Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes](https://mlsanity.com/paper/2312.09881/) (2023)
- [Federated Learning for Medical Image Classification: A Comprehensive Benchmark](https://mlsanity.com/paper/2504.05238/) (2025)
- [UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration](https://mlsanity.com/paper/2501.11388/) (2025)
- [Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes](https://mlsanity.com/paper/2107.11954/) (2021)
- [STDLens: Model Hijacking-Resilient Federated Learning for Object Detection](https://mlsanity.com/paper/2303.11511/) (2023)
- [On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions](https://mlsanity.com/paper/2306.01431/) (2023)
- [Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images](https://mlsanity.com/paper/2205.15530/) (2022)
- [ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models and Large Language Models](https://mlsanity.com/paper/2406.12044/) (2024)
- [MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning](https://mlsanity.com/paper/2409.06067/) (2024)
- [FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning](https://mlsanity.com/paper/2205.01310/) (2022)
- [A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling](https://mlsanity.com/paper/2206.00007/) (2022)
- [Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization](https://mlsanity.com/paper/2211.01883/) (2022)

## Abstract (arXiv)

The outbreak of COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. To aid and accelerate the diagnosis process, automatic diagnosis of COVID-19 via deep learning models has recently been explored by researchers across the world. While different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19, the data itself is still scarce due to patient privacy concerns. Federated Learning (FL) is a natural solution because it allows different organizations to cooperatively learn an effective deep learning model without sharing raw data. However, recent studies show that FL still lacks privacy protection and may cause data leakage. We investigate this challenging problem by proposing a simple yet effective algorithm, named Federated Learning on Medical Datasets using Partial Networks (FLOP), that shares only a partial model between the server and clients. Extensive experiments on benchmark data and real-world healthcare tasks show that our approach achieves comparable or better performance while reducing the privacy and security risks. Of particular interest, we conduct experiments on the COVID-19 dataset and find that our FLOP algorithm can allow different hospitals to collaboratively and effectively train a partially shared model without sharing local patients' data.