Federated learning with swarm intelligence for efficient and secure medical image analysis

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: COVID-19 detection: 96.71%, monkeypox classification: 96.06%, breast cancer diagnosis: 97.0%

• Primary Outcome: Classification accuracy across three medical imaging tasks

• Nnt Or Sensitivity: Communication rounds reduced by 25-30%, accuracy maintained above 94% in privacy-utility analysis

• Confidence Interval: Not reported

Clinical Application

Requires significant computational infrastructure and technical expertise; practical implementation challenges not fully addressed Relevant to Australian healthcare given strict privacy regulations and distributed healthcare system, but would require TGA approval for clinical use and compliance with Privacy Act 1988 Healthcare institutions seeking collaborative AI development while maintaining data privacy, particularly smaller healthcare settings

Abstract

Collaborative learning in healthcare faces challenges, including strict regulations and fragmented data. This research introduces a federated learning framework that employs swarm intelligence to augment communication and enhance the analysis of medical images. The method optimizes hyperparameters, selects features, and assigns aggregation weights to federated clients simultaneously by combining Particle Swarm Optimization (PSO) and the Firefly Algorithm (FA) with deep Convolutional Neural Networks (CNNs). The framework was tested on three medical datasets: COVID-19 chest X-rays (5,856 images), monkeypox skin images (569 images), and breast cancer mammograms (320 images). These datasets were shared among four fake healthcare institutions. It strives to strike a balance between privacy, communication costs, and classification accuracy. The results showed that the test was 96.71% accurate in detecting COVID-19, 96.06% accurate in classifying monkeypox, and 97.0% accurate in diagnosing breast cancer. The framework was able to handle noise and attacks from individuals who sought to disrupt it, which reduced communication rounds by 25-30%. A privacy-utility analysis revealed that there were acceptable trade-offs, with accuracy remaining above 94%. This study employs robust privacy measures and statistical validation. It also shows how to use medical AI in smaller healthcare settings without putting patients' privacy at risk.

References

1. 1.SayedElahl, M. A., Farouk, R. M., Ali, A. E., & Ahmed, E. (2026). Federated learning with swarm intelligence for efficient and secure medical image analysis. Scientific Reports. https://doi.org/10.7937/K9/TCIA.2016.7O02S9CY