The research, led by Suresh Arumugam of Dayananda Sagar University in Bengaluru together with colleagues from three other Indian institutions, introduces FedXAI-Health, a federated learning framework designed for multi-label thoracic disease classification. In federated learning, the model travels to the data rather than the other way around: each participating hospital trains a shared neural network on its own machines, and only the resulting model updates, never the patient images themselves, are sent to a central server for aggregation. The framework was tested in a simulated network of five hospital clients, each holding a differently distributed slice of the publicly available NIH Chest X-ray 14 dataset.

That heterogeneity is the crux of the problem the researchers set out to solve. In real-world federated networks, no two hospitals see the same mix of diseases, scanner types, or patient demographics, a condition known in machine learning as non-IID data. When local datasets diverge this sharply, the averaged model produced by standard federated algorithms can drift away from what any individual site needs. To simulate this faithfully, the team partitioned the data using Dirichlet distributions with concentration parameters ranging from 0.1, which produces extremely skewed client datasets, to 5.0, which approaches a more balanced split, and validated robustness across the entire spectrum.

The headline result is genuinely counterintuitive. Under identical experimental conditions, using an EfficientNetB0 backbone, a batch size of 512, and the Adam optimiser with a weight decay of 10 to the power of minus 4, the classic federated averaging algorithm known as FedAvg achieved a Macro-AUC of 0.8060, plus or minus 0.0048. That figure not only held up under severe non-IID conditions but actually exceeded the centralised baseline, in which all data would have been pooled on one server, by 2.0 percent. The centralised EfficientNetB0 model managed only 0.7859, plus or minus 0.0142. In other words, the privacy-preserving approach did not merely match the gold standard it is usually measured against; it beat it.

The authors attribute part of this advantage to a phenomenon familiar from the regularisation literature: training across heterogeneous sites forces the model to encounter a wider effective variety of disease presentations during optimisation, which can act as an implicit form of data augmentation and reduce overfitting to any single distribution. The team also benchmarked FedProx, a popular federated variant that adds a proximal term to keep local models from straying too far from the global one. FedProx reached a Macro-AUC of 0.7879, plus or minus 0.0020, respectable but below FedAvg in this setting.

That gap prompted one of the study’s most practically useful findings, an ablation study across five values of FedProx’s regularisation coefficient, mu, spanning 0, 0.001, 0.01, 0.1, and 1.0. Performance degraded monotonically as the coefficient increased, meaning the stronger the algorithm tried to tether local models to the global average, the worse the multi-label classification became. The lesson for practitioners is that minimal regularisation is optimal for multi-label medical imaging under these conditions, a direct challenge to the intuition that heterogeneous data always demands stronger constraints. For clinical teams designing federated deployments, this kind of tuning guidance can save months of trial and error.

Accuracy alone, however, is not enough to earn the trust of radiologists, and this is where the explainability component of FedXAI-Health becomes central. The framework pairs its federated training pipeline with two complementary attribution techniques: SHAP, which assigns each input pixel a contribution score for a given prediction, and Grad-CAM, which produces class-specific heatmap localisations over the image. The researchers compared the maps generated by both methods and confirmed that they produced clinically coherent, disease-specific localisations, meaning the models appeared to base their diagnoses on the actual anatomical regions associated with each condition rather than on spurious correlations such as hospital-specific imaging artefacts.

This matters because multi-label thoracic classification is a genuinely hard task. The NIH Chest X-ray 14 dataset covers fourteen distinct conditions, including cardiomegaly, pleural effusion, pneumonia, and pneumothorax, and a single radiograph can carry several of them simultaneously. A model that flags a disease without showing where it saw the evidence is of limited clinical value, and a federated model whose explanations differ wildly across sites would be even harder to trust. By demonstrating that attribution maps remain coherent across a decentralised, heterogeneous network, the study addresses the trustworthiness question at the same time as the privacy one, rather than treating explainability as an afterthought bolted onto a finished model.

The experimental hygiene underlying these claims deserves attention as well. All results are reported as means with standard deviations across three independent runs, and the dataset was split at the patient level between training and test sets, ensuring zero data leakage, a persistent pitfall in medical imaging research where multiple images of the same patient can silently straddle both sets. The team also ran a supplementary experiment on CIFAR-10 to contextualise the findings beyond the medical domain. Crucially for reproducibility, the complete source code, trained model checkpoints, experiment logs, and result files have been released publicly on GitHub, and the NIH dataset itself is openly available.

The broader significance of the work lies in what it suggests about the future of clinical AI. Federated learning has already been deployed in real hospital networks, most famously in a Nature Medicine study predicting clinical outcomes in COVID-19 patients across twenty institutions, and surveys of the field consistently identify non-IID heterogeneity as its central unsolved challenge. By showing that a well-tuned, lightweight federated setup can outperform centralised training on a benchmark thoracic dataset while producing verifiable explanations, FedXAI-Health strengthens the case that hospitals need not choose between collaboration and confidentiality. The code is open, the data is public, and the recipe is documented, which means other research groups can stress-test the results on their own distributions. If the findings generalise beyond simulated clients to genuine multi-site deployments, the era in which privacy regulations condemned each hospital to train diagnostic AI in isolation may finally be drawing to a close, replaced by networks of institutions that learn together while their patients’ images never leave the building.

Subject of Research: Explainable federated learning for privacy-preserving multi-label thoracic disease classification from chest X-rays under non-IID data distributions

Article Title: Explainable Federated Learning for Trustworthy Thoracic Disease Detection Under Non-IID Data Distributions

Article References: Arumugam, S., Sindhu, A., Saroja, M. N., Kannan, S., & Esakkiammal, A. (2026). Explainable Federated Learning for Trustworthy Thoracic Disease Detection Under Non-IID Data Distributions. Machine Learning, 115(10), Article 228. https://doi.org/10.1007/s10994-026-07174-z

Image Credits: AI Generated

DOI: 10.1007/s10994-026-07174-z

Keywords: federated learning, non-IID data, chest X-ray, explainable AI, SHAP, Grad-CAM, FedAvg, FedProx, EfficientNetB0, privacy-preserving machine learning, thoracic disease detection, NIH Chest X-ray 14