This paper explores the multifaceted consequences of federated unlearning (FU) with data heterogeneity. We introduce key metrics for FU assessment, concentrating on verification, global stability, and local fairness, and investigate the inherent trade-offs. Furthermore, we formulate the unlearning process with data heterogeneity through an optimization framework. Our key contribution lies in a comprehensive theoretical analysis of the trade-offs in FU and provides insights into data heterogeneity's impacts on FU. Leveraging these insights, we propose FU mechanisms to manage the trade-offs, guiding further development for FU mechanisms. We empirically validate that our FU mechanisms effectively balance trade-offs, confirming insights derived from our theoretical analysis.

翻译：摘要：本文探讨了数据异质性下联邦遗忘（FU）的多方面影响。我们引入了FU评估的关键指标，重点关注验证、全局稳定性和局部公平性，并研究了其中固有的权衡关系。此外，我们通过优化框架构建了数据异质性下的遗忘过程。我们的核心贡献在于对FU权衡关系进行了全面的理论分析，并揭示了数据异质性对FU的影响。基于这些见解，我们提出了管理权衡关系的FU机制，为FU机制的进一步发展提供了指导。我们通过实验验证，所提出的FU机制能够有效平衡权衡关系，证实了理论分析得出的见解。