# Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off

arXiv: https://arxiv.org/abs/2402.07002 · HTML: https://mlsanity.com/paper/2402.07002/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 10 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Opacus: User-Friendly Differential Privacy Library in PyTorch](https://mlsanity.com/paper/2109.12298/) (2021)
- [LDP-Fed: Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2006.03637/) (2020)
- [Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm](https://mlsanity.com/paper/1804.03728/) (2018)
- [Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models](https://mlsanity.com/paper/2110.13057/) (2021)
- [Gradient Inversion with Generative Image Prior](https://mlsanity.com/paper/2110.14962/) (2021)
- [Local and Central Differential Privacy for Robustness and Privacy in Federated Learning](https://mlsanity.com/paper/2009.03561/) (2020)
- [Learning with User-Level Privacy](https://mlsanity.com/paper/2102.11845/) (2021)
- [Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication](https://mlsanity.com/paper/2102.04737/) (2021)
- [No Free Lunch Theorem for Security and Utility in Federated Learning](https://mlsanity.com/paper/2203.05816/) (2022)
- [Differentially Private Federated Learning with Local Regularization and Sparsification](https://mlsanity.com/paper/2203.03106/) (2022)
- [Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization](https://mlsanity.com/paper/1708.04181/) (2017)
- [Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning](https://mlsanity.com/paper/2202.05318/) (2022)
- [Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning](https://mlsanity.com/paper/2303.04274/) (2023)
- [Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning](https://mlsanity.com/paper/2309.05505/) (2023)
- [Differentially Private Matrix Completion Revisited](https://mlsanity.com/paper/1712.09765/) (2017)
- [PrivateRec: Differentially Private Training and Serving for Federated News Recommendation](https://mlsanity.com/paper/2204.08146/) (2022)

## Built on by (most cited first, 7 of 7)

- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [FedSheafHN: Personalized Federated Learning on Graph-structured Data](https://mlsanity.com/paper/2405.16056/) (2024)
- [Personalized Subgraph Federated Learning with Sheaf Collaboration](https://mlsanity.com/paper/2508.13642/) (2025)
- [ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning](https://mlsanity.com/paper/2509.13739/) (2025)
- [FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility](https://mlsanity.com/paper/2510.26841/) (2025)
- [Secure-CHG: A Comprehensive Framework for Robust and Fair Federated Learning via Hybrid Defense and Contribution-Aware Trust](https://mlsanity.com/paper/2606.31066/) (2026)
- [Pushing the (Decision) Boundaries: Dynamically Calibrating Differentially Private Noise to Explainability in Federated Learning](https://mlsanity.com/paper/2609.03851/) (2026)

## Abstract (arXiv)

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/FedCEOCollaborate-with-Each-Other.