A Secure and Privacy-Preserving Machine Learning approach for waste management using federated learning

Efficient waste management remains a critical challenge in contemporary urban environments. Our research introduces a comprehensive waste management system integrating smart dustbins with federated learning and deep learning models. Federated learning, a technique that ensures privacy by training machine learning models across multiple decentralized edge devices or servers without revealing local data samples, is at the core of our approach. Operating on federated learning principles, our system employs selected deep learning models (ResNet50, VGG16, InceptionV3, and EfficientNet) for precise garbage categorization. Various optimization algorithms such as FedAvg, FedAMP, FedAdam, and FedMA enable collaborative model training across decentralized devices, eliminating the need to centralize sensitive data. The integration of smart dustbins significantly enhances garbage identification and classification capabilities, leading to improved waste sorting and operational efficiency. The chosen deep learning models play a pivotal role in this process, contributing to the system’s advanced garbage categorization. Furthermore, our federated learning approach ensures privacy preservation by avoiding centralization of sensitive data, aligning with privacy-conscious methodologies. This research represents a notable advancement in modern waste management practices, emphasizing federated principles and privacy-aware methodologies. The findings underscore the potential impact of our system on waste management effectiveness and operational efficiency in urban environments.