Abstract
Cross-site lung histopathology classification must account for stain variation, non-IID client data, missing classes, and the cost of adapting large pathology encoders. This study evaluates FedHisto-PAST v2 for three-way classification of adenocarcinoma (ACA), Normal, and squamous cell carcinoma (SCC). FedHisto-PAST v2 combines a frozen HIBOU-B foundation model with parameter-efficient adaptation, stain-conditioned paired-view prediction and feature consistency, reliability-aware prototype learning, and adaptive federated aggregation. Experiments used a five-client, non-IID, raw-data-local simulation with fixed internal evaluation, client-level analysis, component ablations, communication accounting, and a development-influenced exploratory LungHist700 cohort. All principal methods achieved near- ceiling internal performance, which limited discrimination on the fixed split. On LungHist700, FedHisto- PAST v2 achieved a Macro-F1 of 0.728560 and a balanced accuracy of 0.730454. Higher recognition of Normal and SCC was accompanied by lower ACA recall, and calibration remained imperfect. Prediction-level consistency was the only component with a clearly supported independent contribution in the external ablation analysis. Feature consistency and prototype regularization showed no conclusive independent overall gains in Macro-F1. The framework updated 1.253841% of the model parameters. The results provide exploratory cross-dataset evidence for stain-aware, parameter-efficient federation; they do not establish formal privacy, patient-level independence, prospective deployment, or clinical validation.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Shahriar, M. M., Hafiz, M. M. G., Aloteibi, S., & Moni, M. A. (2026). FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification. https://omanscience.com/en/articles/fedhisto-past-parameter-efficient-stain-aware-federated-learning-for-cross-site-lung-histopathology-classification
MLA 9
Shahriar, Muhammad Muhtasim, et al. "FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification." https://omanscience.com/en/articles/fedhisto-past-parameter-efficient-stain-aware-federated-learning-for-cross-site-lung-histopathology-classification.
Chicago (author–date)
Shahriar, Muhammad Muhtasim, M. M. Golam Hafiz, Saad Aloteibi, and Mohammad Ali Moni. 2026. "FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification." https://omanscience.com/en/articles/fedhisto-past-parameter-efficient-stain-aware-federated-learning-for-cross-site-lung-histopathology-classification.
Harvard
Shahriar, M. M., Hafiz, M. M. G., Aloteibi, S. and Moni, M. A. (2026) 'FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification', Available at: https://omanscience.com/en/articles/fedhisto-past-parameter-efficient-stain-aware-federated-learning-for-cross-site-lung-histopathology-classification.
Vancouver
Shahriar MM, Hafiz MMG, Aloteibi S, Moni MA. FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification. https://omanscience.com/en/articles/fedhisto-past-parameter-efficient-stain-aware-federated-learning-for-cross-site-lung-histopathology-classification
IEEE
M. M. Shahriar, M. M. G. Hafiz, S. Aloteibi, and M. A. Moni, "FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification," https://omanscience.com/en/articles/fedhisto-past-parameter-efficient-stain-aware-federated-learning-for-cross-site-lung-histopathology-classification.