NeurIPS 2025

DictPFL: Efficient and Private Federated Learning
on Encrypted Gradients
Jiaqi Xue1, Mayank Kumar1, Yuzhang Shang1, Shangqian Gao2, Rui Ning3
Mengxin Zheng1, Xiaoqian Jiang4, Qian Lou1
1University of Central Florida
2Florida State University
3Old Dominion University
4University of Texas Health Science Center at Houston
{jiaqi.xue,mayank.kumar,yuzhang.shang,mengxin.zheng,qian.lou}@ucf.edu
sgao@cs.fsu.edu, rning@odu.edu, xiaoqian.jiang@uth.tmc.edu
Abstract
Federated Learning (FL) enables collaborative model training across institutions
without sharing raw data. However, gradient sharing still risks privacy leakage,
such as gradient inversion attacks. Homomorphic Encryption (HE) can secure
aggregation but often incurs prohibitive computational and communication over-
head. Existing HE-based FL methods sit at two extremes: encrypting all gradients
for full privacy at high cost, or partially encrypting gradients to save resources
while exposing vulnerabilities. We present DictPFL, a practical framework that
achieves full gradient protection with minimal overhead. DictPFL encrypts every
transmitted gradient while keeping non-transmitted parameters local, preserving
privacy without heavy computation. It introduces two key modules: Decompose-
for-Partial-Encrypt (DePE), which decomposes model weights into a static dic-
tionary and an updatable lookup table—only the latter is encrypted and aggregated,
while the static dictionary remains local and requires neither sharing nor encryp-
tion; and Prune-for-Minimum-Encrypt (PrME), which applies encryption-aware
pruning to minimize encrypted parameters via consistent, history-guided masks.
Experiments show that DictPFL reduces communication cost by 402–748× and
accelerates training by 28–65× compared to fully encrypted FL, while outper-
forming state-of-the-art selective encryption methods by 51–155× in overhead
and 4–19× in speed. Remarkably, DictPFL’s runtime is within 2× of plaintext
FL, demonstrating—for the first time—that HE...