Secure and Privacy-Preserving Federated Learning for Next-Generation Underground Mine Safety

TL;DR

This paper introduces FedMining, a privacy-preserving federated learning framework for underground mining safety that uses decentralized encryption and a balancing aggregation to protect data privacy and improve model convergence.

Contribution

FedMining is the first tailored federated learning framework for underground mining that combines decentralized functional encryption with a novel aggregation method to enhance privacy and model performance.

Findings

FedMining effectively safeguards sensor data privacy in underground mining.

The framework achieves high model accuracy with reduced communication overhead.

FedMining demonstrates rapid convergence in real-world mining datasets.

Abstract

Underground mining operations depend on sensor networks to monitor critical parameters such as temperature, gas concentration, and miner movement, enabling timely hazard detection and safety decisions. However, transmitting raw sensor data to a centralized server for machine learning (ML) model training raises serious privacy and security concerns. Federated Learning (FL) offers a promising alternative by enabling decentralized model training without exposing sensitive local data. Yet, applying FL in underground mining presents unique challenges: (i) Adversaries may eavesdrop on shared model updates to launch model inversion or membership inference attacks, compromising data privacy and operational safety; (ii) Non-IID data distributions across mines and sensor noise can hinder model convergence. To address these issues, we propose FedMining--a privacy-preserving FL framework tailored for underground mining. FedMining introduces two core innovations: (1) a Decentralized Functional Encryption (DFE) scheme that keeps local models encrypted, thwarting unauthorized access and inference attacks; and (2) a balancing aggregation mechanism to mitigate data heterogeneity and enhance convergence. Evaluations on real-world mining datasets demonstrate FedMining's ability to safeguard privacy while maintaining high model accuracy and achieving rapid convergence with reduced communication and computation overhead. These advantages make FedMining both secure and practical for real-time underground safety monitoring.