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.

翻译：地下采矿作业依赖传感器网络监测温度、气体浓度及矿工移动等关键参数，以实现及时的危险检测与安全决策。然而，将原始传感器数据传输至中心化服务器进行机器学习模型训练会引发严重的隐私与安全问题。联邦学习通过实现去中心化的模型训练而无需暴露敏感的本地数据，为此提供了一种有前景的替代方案。然而，在地下采矿环境中应用联邦学习面临独特挑战：（一）攻击者可能窃听共享的模型更新以发起模型反演或成员推断攻击，从而危及数据隐私与作业安全；（二）不同矿山间的非独立同分布数据特征及传感器噪声可能阻碍模型收敛。为解决这些问题，我们提出FedMining——一种专为地下采矿设计的隐私保护联邦学习框架。FedMining引入两项核心创新：（1）采用去中心化功能加密方案，使本地模型保持加密状态，有效抵御未授权访问与推断攻击；（2）设计平衡聚合机制以缓解数据异构性并提升收敛效率。基于真实矿山数据集的评估表明，FedMining能够在保障隐私的同时维持高模型精度，并以较低的通信与计算开销实现快速收敛。这些优势使得FedMining成为实时地下安全监测中既安全又实用的解决方案。