Edge-Intelligence based Federated Learning in the Internet of Medical Things
Suresh Chavhan, Beerelly Srinitha, Sikander Kathat, Ashit Kumar Dutta, Joel J. P. C. Rodrigues · 2024
In the evolving healthcare landscape, the Internet of Medical Things (IoMT) enables real-time data collection through connected devices. Concerns about data privacy in electronic health records are driving changes in health assessment. Blended learning is emerging as a solution, allowing simulation training without sharing critical information centrally. The proposed techniques emphasize privacy and efficiency and use federated averaging to analyze edge computation. Smart healthcare addresses the benefits and challenges of integrating AI and edge tech. In this revolutionary approach, federated learning uses server-side federated averaging, combining local client model parameters while reducing edge computation latency and energy consumption The integration of AI and edge technologies not only increases efficiency but also provides forward-looking approaches for personalized and responsive healthcare. Experimental validation with the Covid-pneumonia dataset highlights the effectiveness of the integrated learning approach, confirming the important contribution to privacy protection and efficient machine learning applications in computing in healthcare of the policies established in countries.