Privacy-preserving Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models without exposing raw data, but exchanged model updates remain vulnerable to inference attacks from honest-but-curious servers. Homomorphic Encryption (HE) addresses this by allowing server-side aggregation directly on encrypted updates, with the CKKS scheme being particularly suitable due to its native support for approximate floating-point arithmetic. However, no prior work has examined how to configure CKKS for PFL deployments, leaving practitioners without principled guidance on parameter selection that directly affects privacy, precision, and computational cost. This paper presents pFedCKKS, a generic framework integrating CKKS into PFL, and provides the first systematic parameter selection guide for practitioners. We derive the full CKKS parameter constraints under 128-bit security for the PFL setting, showing the selection problem reduces to choosing just two values: the inner and outer ciphertext prime. Implemented using the Flower framework and TenSEAL library, pFedCKKS is evaluated on the FEMNIST, CelebA and Sentiment140 datasets with FedFinetune, Ditto and FedPer which represents PFL algorithms. Experimental results reveal an empirical trade-off between precision and computational/communication costs. This allows us to draw a concrete guideline for selecting proper CKKS parameters that balance efficiency and accuracy in real-world deployments of pFedCKKS.

翻译：隐私保护的个性化联邦学习（PFL）使客户端能够在不暴露原始数据的情况下协同训练个性化模型，但交换的模型更新仍易受到诚实但好奇的服务器的推理攻击。同态加密（HE）通过在服务器端直接对加密更新进行聚合来解决这一问题，其中CKKS方案因其原生支持近似浮点运算而特别适用。然而，尚无先前工作研究如何为PFL部署配置CKKS，导致从业者在直接影响隐私、精度和计算成本的参数选择上缺乏原则性指导。本文提出pFedCKKS——一个将CKKS集成到PFL中的通用框架，并首次为从业者提供系统化的参数选择指南。我们推导出PFL场景下128位安全强度的完整CKKS参数约束条件，表明参数选择问题可简化为仅选择两个值：内部密文素数和外部密文素数。基于Flower框架和TenSEAL库实现的pFedCKKS，在使用FedFinetune、Ditto和FedPer（代表PFL算法）的FEMNIST、CelebA和Sentiment140数据集上进行了评估。实验结果揭示了精度与计算/通信成本之间的经验性权衡。这使我们能够为在实际部署pFedCKKS时选择平衡效率与准确性的合适CKKS参数制定具体准则。