The Potential of Federated Learning to Improve Biomedical Waste Management – A Review

Abstract

Biomedical waste management is a crucial aspect of healthcare, which requires proper handling and disposal to prevent the spread of infections and protect the environment. The classification of biomedical waste is a key step in the waste management process, which involves identifying and segregating different types of waste based on their properties and risk level. Despite the regular training of human resources, there exist a chance of human-error in biomedical waste classification. Mismanagement of biomedical waste can be hazardous. The use of machine learning (ML) and federated learning (FL) techniques has the potential to improve the accuracy and efficiency of biomedical waste classification.

This review paper aims to provide an overview of the recent advancements in biomedical waste classification using ML and FL techniques. The paper begins with an introduction to the concept of biomedical waste and its classification system, including the color-coding scheme used for segregation.