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

arXiv: https://arxiv.org/abs/2512.08862 · HTML: https://mlsanity.com/paper/2512.08862/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption](https://mlsanity.com/paper/2104.06824/) (2021)
- [FLCert: Provably Secure Federated Learning against Poisoning Attacks](https://mlsanity.com/paper/2210.00584/) (2022)
- [Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2005.11901/) (2020)
- [Information Stealing in Federated Learning Systems Based on Generative Adversarial Networks](https://mlsanity.com/paper/2108.00701/) (2021)
- [DIS-Mine: Instance Segmentation for Disaster-Awareness in Poor-Light Condition in Underground Mines](https://mlsanity.com/paper/2411.13544/) (2024)
- [CAV-AD: A Robust Framework for Detection of Anomalous Data and Malicious Sensors in CAV Networks](https://mlsanity.com/paper/2407.05461/) (2024)
- [Detecting Untargeted Attacks and Mitigating Unreliable Updates in Federated Learning for Underground Mining Operations](https://mlsanity.com/paper/2508.10212/) (2025)

## Abstract (arXiv)

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.