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Integrating Visual Intelligence With Federated Learning and IoT in Healthcare
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Author(s): G. K. Shwetha (Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India), Ashish Avasthi (Faculty of Computer Engineering, Poornima University, Jaipur, India), Manish Kumar (Department Faculty of Computer Engineering, Poornima University, Jaipur, India), Saurabh Chandra (School of Law, Bennett University, Greater Noida, India)and B. S. Hari (Department of Mechanical Engineering, Kongu Engineering College, Erode, India)
Copyright: 2026
Pages: 30
Source title:
Combining Visual Intelligence and Federated Learning in Smart Healthcare
Source Author(s)/Editor(s): Manisha Guduri (Lawrence Technological University, USA), Chinmay Chakraborty (Kalinga Institute of Industrial Technology, India)and Martin Margala (University of Louisiana at Lafayette, USA)
DOI: 10.4018/979-8-3693-6094-1.ch006
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Abstract
This chapter discusses how visual intelligence can be integrated with federated learning and IoT technologies in the healthcare segment. Health care continues to rely on advanced data analytics more and more, making visual intelligence a key player in interpreting complex medical images and data. Federated learning then strengthens this by facilitating collaborative model training across decentralized devices while preserving patient privacy and data security. This enables the use of a combination of data from various sources without compromising confidential information. Multiple applications are discussed, including remote patient monitoring and diagnostic imaging, which highlight exactly how the use of these technologies can improve patient outcomes, make treatment more personalized, and streamline health care flow management. This integration leverages the strengths of visual intelligence, federated learning, and IoT to address pertinent challenges in today's healthcare environment.
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