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Article History

Received: 22 September 2024

Accepted: 24 April 2026

First Online: 11 May 2026

Declarations

:

: The authors declare no competing interests.

: The authors confirm that this work complies with ethical standards and applicable regulations. This study utilizes publicly available datasets, specifically the COVID-19 image data, Monkeypox image data, and Breast Cancer Wisconsin (Diagnostic) data, all of which were collected and shared according to relevant ethical guidelines and regulations as verified by their respective providers: The COVID-19 dataset was made available by Paul Timothy Mooney on Kaggle for research purposes and has been widely used in the academic community for developing diagnostic algorithms. The Monkeypox dataset, also publicly accessible on Kaggle, was provided by Nafin Hossain and contains anonymized dermatological images suitable for machine learning research. The Breast Cancer Wisconsin dataset can be accessed through the UCI Machine Learning Repository and represents one of the most established benchmarks in medical machine learning research. Since this study relies exclusively on publicly available data and does not involve direct interaction with human participants, no new experiments were conducted involving human participants or their data. The research methodology focuses on algorithmic development and performance evaluation using existing anonymized datasets. Consequently, no additional institutional review board approval or informed consent procedures were necessary. The federated learning framework developed in this study ensures that even when applied to new datasets in clinical settings, patient privacy is preserved through the fundamental design principle that raw medical data never leaves the local healthcare institution. This approach aligns with current privacy regulations including HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation) requirements for medical data handling. All experimental procedures were conducted in accordance with the Declaration of Helsinki principles for ethical medical research, adapted for computational studies using publicly available medical imaging data.

: All authors consent to the publication of this manuscript and confirm that the work presented herein has not been published previously and is not under consideration for publication elsewhere.