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：本文探讨了数据异构性背景下联邦遗忘（FU）的多重影响。我们引入用于FU评估的关键指标，聚焦于验证、全局稳定性与局部公平性，并研究其内在的权衡关系。此外，我们通过优化框架形式化描述了数据异构性下的遗忘过程。我们的核心贡献在于对FU中的权衡关系进行了全面的理论分析，并揭示了数据异构性对FU的影响机理。基于这些洞见，我们提出用于管理权衡的FU机制，为FU机制的进一步发展提供指导。实验验证表明，我们提出的FU机制能够有效平衡各类权衡，证实了理论分析所得的洞察结论。