Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning

2026-06-07

AI Summary

This work addresses the lack of systematic parameter configuration guidelines for CKKS homomorphic encryption in privacy-preserving personalized federated learning, where balancing security, accuracy, and efficiency remains challenging. The authors propose pFedCKKS, the first framework to establish a complete set of CKKS parameter constraints tailored to personalized federated learning under 128-bit security, reducing complex parameter selection to determining the inner and outer ciphertext primes. Implemented using Flower and TenSEAL and integrated with algorithms such as FedPer, Ditto, and FedFinetune, extensive experiments on FEMNIST, CelebA, and Sentiment140 empirically reveal the trade-offs between model accuracy and computational/communication overheads induced by parameter choices, yielding practical configuration guidelines for real-world deployment.

Abstract

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.

Problem

• CKKS
• Personalized Federated Learning
• Homomorphic Encryption
• Parameter Selection
• Privacy-Preserving

Innovation

• CKKS
• Personalized Federated Learning
• Homomorphic Encryption
• Parameter Selection
• Privacy-Preserving