Edge-Assisted Framework for Malaria Parasite Detection on Cell Images Using Federated Learning
Amrita Rajput, Shikhar Saxena, Mainak Adhikari · 2023
Malaria is a serious and fatal disease spread by female Anopheles. In past decades, the Deep Learning models played the most powerful roles for malaria parasite detection. However, concerning the privacy of patient medical records, prevents data sharing in medical institutions, resulting in the unexpected performance of Deep Neural Network models. Thankfully, Federated Learning (FL) provides the opportunity to train models locally and build the global model based on the updates received from local client models without sharing data. The traditional Federated Averaging algorithm assumes equal contributions from all participants in building the global model. Therefore, it limits the different applications by ignoring the potential domain of several different FL participants. The challenges mentioned above motivate us to propose a framework of FL using customized Weighted Averaging for Malaria Parasite Detection using Cell Microscopic (CMR) Images, collected from different medical institutions at the edge using a Convolutional Neural Network, named FedWtAvg. The proposed FedWtAvg provides the advantage of giving higher priority to participants who have large amounts of data and can build an effective global model. Extensive experiments were conducted on the CMR Images dataset to demonstrate that FedWtAvg outperforms and improves the accuracy by around 1.21 % over existing ones without data sharing and real-time continuous learning.