02178nas a2200265   4500000000100000000000100001008004100002260001200043653002200055653002500077653002200102653002700124653003300151100002000184700002600204700002000230700002800250700001700278245012700295856005800422300000900480490001300489520139600502022001401898       9998                            d  c10/202410aData Augmentation10aData Confidentiality10aDisease Diagnosis10aCollaborative Learning10aConvolutional Neural Network1 aSujit Kumar Das1 aNageswara Rao Moparth1 aSuyel Namasudra1 aRubén González-Crespo1 aDavid Taniar00aA Smart Healthcare System Using Consumer Electronics and Federated Learning to Automatically Diagnose Diabetic Foot Ulcers  uhttps://www.ijimai.org/journal/bibcite/reference/3502  a1-130 vIn press3 aPrivacy breaches on sensitive and widely distributed health data in consumer electronics (CE) demand novel strategies to protect privacy with correctness and proper operation maintenance. This work presents a scalable Federated Learning (FL) framework-based smart healthcare approach. Remote medical facilities frequently struggle with imbalanced datasets, including intermittent client connections to the FL global server. The proposed approach handled intermittent clients with diabetic foot ulcers (DFU) images. A data augmentation approach proposes to handle class imbalance problems during local model training. Also, a novel Convolutional Neural Network (CNN) architecture, ResKNet (K=4), is designed for client-side model training. The ResKNet is a sequence of distinctive residual blocks with 2D convolution, batch normalization, LeakyReLU activation, and skip connections (convolutional and identity). The proposed approach is evaluated for various client counts (5,10,15, and 20) and multiple test dataset sizes. The proposed framework can leverage consumer electronic devices and ensure secure data sharing among multiple sources. The potential of integrating the proposed approach with smartphones and wearable devices to provide highly secure data transmission is very high. The approach also helps medical institutions collaborate and develop a robust patient diagnostic model.  a1989-1660