面向纵向联邦学习的公平高效贡献评估
Fair and efficient contribution valuation for vertical federated learning
AI总结：
本文针对纵向联邦学习中数据源贡献难以公平评估的问题，提出基于Shapley值的VerFedSV指标，在保证公平性的同时实现高效计算，并适配同步与异步算法，实验验证了其公平性、高效性、适应性和有效性。
摘要：
联邦学习是一项新兴技术，用于在分散的数据源上训练机器学习模型而无需共享数据。纵向联邦学习，也称为基于特征的联邦学习，适用于数据源具有相同样本标识但特征集不同的场景。为确保数据所有者之间的公平性，客观评估不同数据源的贡献并相应补偿对应的数据所有者至关重要。Shapley值是一种源自合作博弈论的可证明公平的贡献评估指标。然而，其直接计算需要在每种潜在的数据源组合上反复重新训练模型，由于多轮联邦学习导致通信和计算开销过高而难以承受。为应对这一挑战，我们基于经典Shapley值提出了一种名为纵向联邦Shapley值（VerFedSV）的贡献评估指标。我们证明VerFedSV不仅满足许多理想的公平性性质，而且计算高效。此外，VerFedSV可适配同步和异步纵向联邦学习算法。理论分析和大量实验结果均证明了VerFedSV的公平性、高效性、适应性和有效性。
英文摘要：
Federated learning is an emerging technology for training machine learning models across decentralized data sources without sharing data. Vertical federated learning, also known as feature-based federated learning, applies to scenarios where data sources have the same sample IDs but different feature sets. To ensure fairness among data owners, it is critical to objectively assess the contributions from different data sources and compensate the corresponding data owners accordingly. The Shapley value is a provably fair contribution valuation metric originating from cooperative game theory. However, its straight-forward computation requires extensively retraining a model on each potential combination of data sources, leading to prohibitively high communication and computation overheads due to multiple rounds of federated learning. To tackle this challenge, we propose a contribution valuation metric called vertical federated Shapley value (VerFedSV) based on the classic Shapley value. We show that VerFedSV not only satisfies many desirable properties of fairness but is also efficient to compute. Moreover, VerFedSV can be adapted to both synchronous and asynchronous vertical federated learning algorithms. Both theoretical analysis and extensive experimental results demonstrate the fairness, efficiency, adaptability, and effectiveness of VerFedSV.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
