ICML 2025

Clients Collaborate: Flexible Differentially Private Federated Learning with
Guaranteed Improvement of Utility-Privacy Trade-off
Yuecheng Li 1 Lele Fu 1 Tong Wang 2 Jian Lou 1 Bin Chen 3 Lei Yang 1 Jian Shen 4 Zibin Zheng 1
Chuan Chen 1
Abstract
To defend against privacy leakage of user data,
differential privacy is widely used in federated
learning, but it is not free.
The addition of
noise randomly disrupts the semantic integrity
of the model and this disturbance accumulates
with increased communication rounds. In this
paper, we introduce a novel federated learning
framework with rigorous privacy guarantees,
named FedCEO, designed to strike a trade-off
between model utility and user privacy by
letting clients “Collaborate with Each Other”.
Specifically, we perform efficient tensor low-rank
proximal optimization on stacked local model
parameters at the server, demonstrating its
capability to flexibly truncate high-frequency
components in spectral space. This capability
implies that our FedCEO can effectively recover
the disrupted semantic information by smoothing
the global semantic space for different privacy
settings
and
continuous
training
processes.
Moreover, we improve the SOTA utility-privacy
trade-off bound by order of
√
d, where d is the
input dimension. We illustrate our theoretical
results
with
experiments
on
representative
datasets and observe significant performance
improvements and strict privacy guarantees under
different privacy settings. The code is available at
https://github.com/6lyc/FedCEO_
Collaborate-with-Each-Other.
1Sun Yat-sen University, Guangzhou, China 2Texas A&M Uni-
versity, Texas, USA 3Harbin Institute of Technology, Shenzhen,
China 4Zhejiang Sci-Tech University, China. Correspondence to:
Chuan Chen <chenchuan@mail.sysu.edu.cn>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the author(s).
1. Introduction
Federated learning (FL) (McMahan et al., 2017), a priva...

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