A Federated Learning-Integrated Autoencoder Model for Robust and Decentralized Pneumonia Detection in Chest X-Rays
Bibliographic record
Abstract
A novel pneumonia detection system integrates Federated Learning (FL) with autoencoders to address data scarcity and privacy concerns commonly faced in medical diagnostics.Traditional pneumonia detection relies on supervised learning methods primarily Convolutional Neural Networks (CNNs) and transfer learning which require large, labelled datasets stored centrally, raising significant ethical and privacy challenges.In contrast, the proposed system leverages FL to enable collaborative model training across multiple medical institutions without sharing sensitive patient data.Autoencoders further enhance the model's ability to learn effectively from limited labelled data, improving its generalization in real-world clinical settings.Performance evaluations demonstrate that this approach outperforms existing models in detecting pneumonia from chest X-ray images, achieving superior accuracy, precision, recall, and F1-score.Specifically, the model reaches an accuracy of 95.15%, a precision of 95.8%, and a recall of 98.35%, significantly exceeding results from conventional CNN and transfer learning-based methods.The system not only delivers high diagnostic accuracy but also promotes ethical data handling by eliminating the need for centralized data storage.Overall, this solution addresses the critical limitations of traditional diagnostic frameworks and sets the foundation for secure, privacy-preserving, AI-driven clinical tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".