# Enhancing Privacy in Federated Learning through Local Training

arXiv: https://arxiv.org/abs/2403.17572 · HTML: https://mlsanity.com/paper/2403.17572/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 64 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 30 of 37)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [The Composition Theorem for Differential Privacy](https://mlsanity.com/paper/1311.0776/) (2013)
- [Differentially Private Learning with Adaptive Clipping](https://mlsanity.com/paper/1905.03871/) (2019)
- [Privacy and Robustness in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/2012.06337/) (2020)
- [FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data](https://mlsanity.com/paper/2005.11418/) (2020)
- [ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!](https://mlsanity.com/paper/2202.09357/) (2022)
- [Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients](https://mlsanity.com/paper/2102.07053/) (2021)
- [FedSplit: An algorithmic framework for fast federated optimization](https://mlsanity.com/paper/2005.05238/) (2020)
- [Privacy Amplification by Iteration](https://mlsanity.com/paper/1808.06651/) (2018)
- [A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent](https://mlsanity.com/paper/1905.11261/) (2019)
- [Differentially Private Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2111.09278/) (2021)
- [Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent](https://mlsanity.com/paper/2102.05855/) (2021)
- [Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss](https://mlsanity.com/paper/2205.13710/) (2022)
- [A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates](https://mlsanity.com/paper/1704.07807/) (2017)
- [Federated Learning: A Signal Processing Perspective](https://mlsanity.com/paper/2103.17150/) (2021)
- [Acceleration Methods](https://mlsanity.com/paper/2101.09545/) (2021)
- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [Joint Privacy Enhancement and Quantization in Federated Learning](https://mlsanity.com/paper/2208.10888/) (2022)
- [Towards Efficient Communications in Federated Learning: A Contemporary Survey](https://mlsanity.com/paper/2208.01200/) (2022)
- [Local Exact-Diffusion for Decentralized Optimization and Learning](https://mlsanity.com/paper/2302.00620/) (2023)
- [Can 5th Generation Local Training Methods Support Client Sampling? Yes!](https://mlsanity.com/paper/2212.14370/) (2022)
- [Privacy-Aware Compression for Federated Data Analysis](https://mlsanity.com/paper/2203.08134/) (2022)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Operator Splitting Performance Estimation: Tight contraction factors and optimal parameter selection](https://mlsanity.com/paper/1812.00146/) (2018)
- [Differentially Private Distributed Constrained Optimization](https://mlsanity.com/paper/1411.4105/) (2014)
- [Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications](https://mlsanity.com/paper/2010.00848/) (2020)
- [A Review of Federated Learning in Energy Systems](https://mlsanity.com/paper/2208.10941/) (2022)
- [A Snapshot of the Frontiers of Client Selection in Federated Learning](https://mlsanity.com/paper/2210.04607/) (2022)

## Built on by (most cited first, 3 of 3)

- [A survey on secure decentralized optimization and learning](https://mlsanity.com/paper/2408.08628/) (2024)
- [Convergent Differential Privacy Analysis for General Federated Learning](https://mlsanity.com/paper/2408.15621/) (2024)
- [Communication-Efficient Learning for Satellite Constellations](https://mlsanity.com/paper/2511.20220/) (2025)

## Abstract (arXiv)

In this paper we propose the federated learning algorithm Fed-PLT to overcome the challenges of (i) expensive communications and (ii) privacy preservation. We address (i) by allowing for both partial participation and local training, which significantly reduce the number of communication rounds between the central coordinator and computing agents. The algorithm matches the state of the art in the sense that the use of local training demonstrably does not impact accuracy. Additionally, agents have the flexibility to choose from various local training solvers, such as (stochastic) gradient descent and accelerated gradient descent. Further, we investigate how employing local training can enhance privacy, addressing point (ii). In particular, we derive differential privacy bounds and highlight their dependence on the number of local training epochs. We assess the effectiveness of the proposed algorithm by comparing it to alternative techniques, considering both theoretical analysis and numerical results from a classification task.