# Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning

arXiv: https://arxiv.org/abs/2202.05318 · HTML: https://mlsanity.com/paper/2202.05318/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 42 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 30 of 33)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Personalized Cross-Silo Federated Learning on Non-IID Data](https://mlsanity.com/paper/2007.03797/) (2020)
- [Improving Federated Learning Personalization via Model Agnostic Meta Learning](https://mlsanity.com/paper/1909.12488/) (2019)
- [Federated Learning of a Mixture of Global and Local Models](https://mlsanity.com/paper/2002.05516/) (2020)
- [Federated Multi-Task Learning under a Mixture of Distributions](https://mlsanity.com/paper/2108.10252/) (2021)
- [Adaptive Gradient-Based Meta-Learning Methods](https://mlsanity.com/paper/1906.02717/) (2019)
- [Private Stochastic Convex Optimization: Optimal Rates in Linear Time](https://mlsanity.com/paper/2005.04763/) (2020)
- [Is Local SGD Better than Minibatch SGD?](https://mlsanity.com/paper/2002.07839/) (2020)
- [Federated Evaluation of On-device Personalization](https://mlsanity.com/paper/1910.10252/) (2019)
- [Understanding Gradient Clipping in Private SGD: A Geometric Perspective](https://mlsanity.com/paper/2006.15429/) (2020)
- [Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications](https://mlsanity.com/paper/2102.08503/) (2021)
- [Federated Reconstruction: Partially Local Federated Learning](https://mlsanity.com/paper/2102.03448/) (2021)
- [Decentralized Collaborative Learning of Personalized Models over Networks](https://mlsanity.com/paper/1610.05202/) (2016)
- [Differentially Private Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2111.09278/) (2021)
- [Learning with User-Level Privacy](https://mlsanity.com/paper/2102.11845/) (2021)
- [Federated Mutual Learning](https://mlsanity.com/paper/2006.16765/) (2020)
- [Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes](https://mlsanity.com/paper/1805.10074/) (2018)
- [Personalized and Private Peer-to-Peer Machine Learning](https://mlsanity.com/paper/1705.08435/) (2017)
- [Differentially Private Meta-Learning](https://mlsanity.com/paper/1909.05830/) (2019)
- [Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques](https://mlsanity.com/paper/2102.09743/) (2021)

## Built on by (most cited first, 23 of 23)

- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning](https://mlsanity.com/paper/2307.09619/) (2023)
- [On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation](https://mlsanity.com/paper/2306.09104/) (2023)
- [Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates](https://mlsanity.com/paper/2402.12780/) (2024)
- [Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning](https://mlsanity.com/paper/2309.05505/) (2023)
- [Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off](https://mlsanity.com/paper/2402.07002/) (2024)
- [Balancing Privacy Protection and Interpretability in Federated Learning](https://mlsanity.com/paper/2302.08044/) (2023)
- [Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients](https://mlsanity.com/paper/2409.20329/) (2024)
- [Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models](https://mlsanity.com/paper/2501.13904/) (2025)
- [Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning](https://mlsanity.com/paper/2311.18190/) (2023)
- [Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data](https://mlsanity.com/paper/2308.04755/) (2023)
- [Semi-Variance Reduction for Fair Federated Learning](https://mlsanity.com/paper/2406.16193/) (2024)
- [Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees](https://mlsanity.com/paper/2410.08934/) (2024)
- [DP2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization](https://mlsanity.com/paper/2409.13645/) (2024)
- [FedTLU: Federated Learning with Targeted Layer Updates](https://mlsanity.com/paper/2412.17692/) (2024)
- [Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates](https://mlsanity.com/paper/2503.09192/) (2025)
- [An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy](https://mlsanity.com/paper/2503.20768/) (2025)
- [Personalized Federated Training of Diffusion Models with Privacy Guarantees](https://mlsanity.com/paper/2504.00952/) (2025)
- [Personalized and Resilient Distributed Learning Through Opinion Dynamics](https://mlsanity.com/paper/2505.14081/) (2025)
- [ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push](https://mlsanity.com/paper/2510.20157/) (2025)
- [FedHypeVAE: Federated Learning with Hypernetwork Generated Conditional VAEs for Differentially Private Embedding Sharing](https://mlsanity.com/paper/2601.00785/) (2026)
- [Tackling Privacy Heterogeneity in Differentially Private Federated Learning](https://mlsanity.com/paper/2602.22633/) (2026)
- [Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation](https://mlsanity.com/paper/2604.07125/) (2026)

## Abstract (arXiv)

Large-scale machine learning systems often involve data distributed across a collection of users. Federated learning algorithms leverage this structure by communicating model updates to a central server, rather than entire datasets. In this paper, we study stochastic optimization algorithms for a personalized federated learning setting involving local and global models subject to user-level (joint) differential privacy. While learning a private global model induces a cost of privacy, local learning is perfectly private. We provide generalization guarantees showing that coordinating local learning with private centralized learning yields a generically useful and improved tradeoff between accuracy and privacy. We illustrate our theoretical results with experiments on synthetic and real-world datasets.