解决联邦图神经网络中缺失邻居信息导致的预测不确定性问题
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information
- 用变分自编码器重建缺失邻居，缓解数据不完整影响
- 在真实数据集上实现更小的预测集合且保持覆盖率保证
- 适合关注联邦学习安全性和可靠性研究者参考
图在数据挖掘与机器学习中至关重要，用于表示现实世界对象及其交互。随着图数据集规模增长，管理大规模分散的子图变得关键，尤其在联邦学习框架下。此类框架面临严重挑战，包括缺失邻居信息，可能危及安全关键场景中的模型可靠性。部署此类训练模型需量化不确定性。本研究将已建立的不确定性量化方法——共形预测（Conformal Prediction, CP）扩展至联邦图学习。针对分布式子图中的缺失链接问题，提出方法以最小化其对CP预测集大小的负面影响。分析了分布式子图间的数据依赖性，建立了CP有效性和精确测试时覆盖率的条件。引入基于变分自编码器的缺失邻居重建方法，减轻缺失数据的负面影响。在真实世界数据集上的实证评估表明，该方法可生成更小的预测集，同时确保覆盖率保证。
原文摘要 · Abstract (English)
Graphs play a crucial role in data mining and machine learning, representing real-world objects and interactions. As graph datasets grow, managing large, decentralized subgraphs becomes essential, particularly within federated learning frameworks. These frameworks face significant challenges, including missing neighbor information, which can compromise model reliability in safety-critical settings. Deployment of federated learning models trained in such settings necessitates quantifying the uncertainty of the models. This study extends the applicability of Conformal Prediction (CP), a well-established method for uncertainty quantification, to federated graph learning. We specifically tackle the missing links issue in distributed subgraphs to minimize its adverse effects on CP set sizes. We discuss data dependencies across the distributed subgraphs and establish conditions for CP validity and precise test-time coverage. We introduce a Variational Autoencoder-based approach for reconstructing missing neighbors to mitigate the negative impact of missing data. Empirical evaluations on real-world datasets demonstrate the efficacy of our approach, yielding smaller prediction sets while ensuring coverage guarantees.
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