Oct 2026· Vol 57, pp. 1864 - 1910· 0 citations· 18 references
TL;DR
A novel Federated Transformer-Based Dual Attention Volatility Network for lung cancer classification implements a decentralized federated learning architecture to ensure privacy-preserving model training over multiple clients.
An implementation of federated learning is presented that addresses challenges of collaborative model training among simulated distributed client nodes while ensuring that raw patient data remains confidential by enabling collaborative model training among simulated distributed client nodes while ensuring that raw pati...
B. Shrimali, Rebekah Geddam, H. Ghayvat et al.· Healthcare technology letter...· 0 citations
The present findings demonstrate reduced centralized raw-data exposure and promising computational characteristics, while edge-device validation and stronger privacy-preserving mechanisms remain important directions for future work.
Zhi Gong, Nai-Xue Xiong, Cheng-Lin Zhao et al.· Frontiers in Public Health· 0 citations
The suggested framework serves as an accurate, scalable, and privacy-preserving approach for distributed medical imaging and aggregates only the model parameters at the central server rather than the raw medical data.
Guda Madhu, Nirmalajyothi Narisetty· International journal of com...· 0 citations
The suggested solution offers a computationally efficient, accurate and interpretable solution to computer-aided diagnosis of gastrointestinal diseases, and represents a step toward computer-aided diagnosis of gastrointestinal diseases, subject to further validation on external, multi-center data.
V. Tanwar, Bhisham Sharma, Julian L. Webber et al.· Frontiers in Artificial Inte...· 0 citations
The new MACBDN approach is highly successful in integrating federated learning, graph representations, and the attention mechanism to maintain an optimal balance between the three parameters, and its lightweight nature and privacy-preserved design help it stay flexible for practical clinical settings and multi-party ho...
Asha Latha Thandu, Pradeepini Gera· The European Physical Journa...· 0 citations
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.