# Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

arXiv: https://arxiv.org/abs/2111.09461 · HTML: https://mlsanity.com/paper/2111.09461/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 59 references that are in the map.
- First posted on arXiv 18 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [DropBlock: A regularization method for convolutional networks](https://mlsanity.com/paper/1810.12890/) (2018)
- [Breaking the cycle -- Colleagues are all you need](https://mlsanity.com/paper/1911.10538/) (2019)
- [Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?](https://mlsanity.com/paper/2004.04968/) (2020)

## Built on by (most cited first, 18 of 18)

- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [FedHCA2: Towards Hetero-Client Federated Multi-Task Learning](https://mlsanity.com/paper/2311.13250/) (2023)
- [Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine](https://mlsanity.com/paper/2211.09317/) (2022)
- [FedSampling: A Better Sampling Strategy for Federated Learning](https://mlsanity.com/paper/2306.14245/) (2023)
- [Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcare](https://mlsanity.com/paper/2405.07735/) (2024)
- [Privacy-Preserving Biometric Verification with Handwritten Random Digit String](https://mlsanity.com/paper/2503.12786/) (2025)
- [FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification](https://mlsanity.com/paper/2302.12662/) (2023)
- [Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2402.12876/) (2024)
- [Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification](https://mlsanity.com/paper/2406.11310/) (2024)
- [Collective Intelligent Strategy for Improved Segmentation of COVID-19 from CT](https://mlsanity.com/paper/2212.12264/) (2022)
- [Medical Federated Model with Mixture of Personalized and Sharing Components](https://mlsanity.com/paper/2306.14483/) (2023)
- [Generative AI in Medicine](https://mlsanity.com/paper/2412.10337/) (2024)
- [Towards Unified Modeling in Federated Multi-Task Learning via Subspace Decoupling](https://mlsanity.com/paper/2505.24185/) (2025)
- [Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by Integrating Expert Knowledge and Interpretable Data-driven Intelligence](https://mlsanity.com/paper/2401.08695/) (2024)
- [Decentralized EM Algorithm for Gaussian Mixtures under Data Heterogeneity and Partial Labeling](https://mlsanity.com/paper/2411.05591/) (2024)
- [Mutually Causal Semantic Distillation Network for Zero-Shot Learning](https://mlsanity.com/paper/2603.17412/) (2026)
- [Benchmark Evaluation of Federated Learning on Multi-organ Images](https://mlsanity.com/paper/2607.08219/) (2026)
- [Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions](https://mlsanity.com/paper/2607.24589/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.