Local E-waste Management Audit and Prediction Model: A Machine Learning Approach

Abstract

This paper addresses the challenge of enhancing local e-waste management through a privacy-preserving, auditable machine learning framework. Recent data show that global electronic waste (e-waste) generation exceeded 62 million tone’s in 2022, with only ~22% properly collected and recycled[1][2]. Managing this growing stream of hazardous waste at the municipal level requires intelligent analytics while respecting data privacy and accountability. We present a new federated blockchain-based framework where municipal offices and recycling centers act as edge nodes. These nodes can train their own predictive models—such as LSTM networks for estimating waste volumes or detecting anomalies—directly on their local e- waste datasets, removing the need to share any raw information [3][4]. Local model updates are encrypted and submitted via smart contracts to a permissioned blockchain, where PBFT-style consensus and immutable logging provide trust and auditability. The global model is updated only from verified contributions, and audit logs record each update for transparent inspection. We simulate this pipeline under various load and threat scenarios. Results show that the federated-blockchain approach achieves strong data privacy and tamper-proof auditability, with predictive accuracy comparable to centralized benchmarks. Table I compares centralized ML, standard federated ML, and federated- blockchain ML across security, auditability, scalability, and privacy, highlighting the improvements of our design. Figure 1 illustrates the system architecture, and Figure 2 shows the increased audit latency due to blockchain overhead. Our findings demonstrate that integrating blockchain with federated learning significantly enhances trust and auditability in local e-waste management systems, without sacrificing predictive performance.