most citedHybridVFL: Disentangled Feature Learning for Edge-Enabled Vertical Federated Multimodal Classification

1 citations · 1 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Exploring the Impact of Skin Color on Skin Lesion Segmentation

Kuniko Paxton, Medina Kapo, Amila Akagić +3

Skin cancer, particularly melanoma, remains a major cause of morbidity and mortality, making early detection critical. AI-driven dermatology systems often rely on skin lesion segme…

cs.AI2026

Evaluating a Multi-Agent Voice-Enabled Smart Speaker for Care Homes: A Safety-Focused Framework

Zeinab Dehghani, Rameez Raja Kureshi, Koorosh Aslansefat +6

Artificial intelligence (AI) is increasingly being explored in health and social care to reduce administrative workload and allow staff to spend more time on patient care. This pap…

cs.LG20251 cited

HybridVFL: Disentangled Feature Learning for Edge-Enabled Vertical Federated Multimodal Classification

Mostafa Anoosha, Zeinab Dehghani, Kuniko Paxton +2

Vertical Federated Learning (VFL) offers a privacy-preserving paradigm for Edge AI scenarios like mobile health diagnostics, where sensitive multimodal data reside on distributed,…

cs.CV2025

Skewness-Guided Pruning of Multimodal Swin Transformers for Federated Skin Lesion Classification on Edge Devices

Kuniko Paxton, Koorosh Aslansefat, Dhavalkumar Thakker +1

In recent years, high-performance computer vision models have achieved remarkable success in medical imaging, with some skin lesion classification systems even surpassing dermatolo…

cs.CV2025

Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting

Kuniko Paxton, Zeinab Dehghani, Koorosh Aslansefat +2

Skin color has historically been a focal point of discrimination, yet fairness research in machine learning for medical imaging often relies on coarse subgroup categories, overlook…

cs.AI2025

Advancing Symbolic Integration in Large Language Models: Beyond Conventional Neurosymbolic AI

Maneeha Rani, Bhupesh Kumar Mishra, Dhavalkumar Thakker

LLMs have demonstrated highly effective learning, human-like response generation,and decision-making capabilities in high-risk sectors. However, these models remain black boxes bec…