Siddiqui, Md. Saiful BariSadeque, Farig YousufRoup, Ryan AzimSultana, SabrinaTahsin, SabaMorshed, MostakimKhanam, Marzia2026-08-092026-08-0920242025-06ID 22101669ID 21101045ID 22101245ID 24141234ID 21201571https://hdl.handle.net/10361/28832This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.Cataloged from PDF version of thesis.Includes bibliographical references (pages 77-81).Skin lesion diagnosis is well-suited to data-driven methods, yet its real-world use is often hindered by strict privacy rules, fragmented data ownership, and inconsistent imaging conditions. To overcome these barriers, our work introduces a robust privacy-preserving framework that combines multimodal representation learning with decentralized training which reduces the need to centralize sensitive clinical data. We propose a unified dual-branch framework that supports (i) task-level multitask learning by coupling lesion classification with lesion segmentation using a shared Swin Transformer encoder and an Attention U-Net decoder, and (ii) modality-level learning by fusing image features with auxiliary patient metadata through configurable fusion strategies. To address client heterogeneity under federated learning, we introduce a Dynamic Uncertainty-Aware Divide2Conquer (DUA-D2C) aggregation strategy that adaptively weights client updates based on predictive performance and uncertainty, improving robustness in non-IID settings. Extensive evaluations on HAM10000 and MILK10K demonstrate that multimodal fusion improves over image-only baselines, and that DUA-D2C preserves or improves performance on HAM10000 while constraining degradation on the more heterogeneous MILK10K setting.91 pagesen-USAttribution-NonCommercial-NoDerivatives 4.0 InternationalBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.http://creativecommons.org/licenses/by-nc-nd/4.0/Skin diseasesDiseases detectionSkin cancerMelanomaDeep learningSwin transformerDual-task learningMultimodal fusionFederated learningU-netUncertainty-aware aggregationSkin--Diseases--Diagnosis.Skin--Cancer--Early detection.Diagnostic imaging--Data processing.Deep learning (Machine learning).Thesis