# Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization

arXiv: https://arxiv.org/abs/2008.01558 · HTML: https://mlsanity.com/paper/2008.01558/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2020.

## Built on by (most cited first, 26 of 26)

- [Differentially Private Federated Learning with Local Regularization and Sparsification](https://mlsanity.com/paper/2203.03106/) (2022)
- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
- [Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning](https://mlsanity.com/paper/2102.12920/) (2021)
- [Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy](https://mlsanity.com/paper/2202.07178/) (2022)
- [Make Landscape Flatter in Differentially Private Federated Learning](https://mlsanity.com/paper/2303.11242/) (2023)
- [Private Non-Convex Federated Learning Without a Trusted Server](https://mlsanity.com/paper/2203.06735/) (2022)
- [Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation](https://mlsanity.com/paper/2304.01541/) (2023)
- [A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency](https://mlsanity.com/paper/2307.10655/) (2023)
- [Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking](https://mlsanity.com/paper/2205.13054/) (2022)
- [FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation](https://mlsanity.com/paper/2212.07224/) (2022)
- [Federated Progressive Sparsification (Purge, Merge, Tune)+](https://mlsanity.com/paper/2204.12430/) (2022)
- [Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy](https://mlsanity.com/paper/2309.03437/) (2023)
- [Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy](https://mlsanity.com/paper/2304.07460/) (2023)
- [Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy](https://mlsanity.com/paper/2305.00873/) (2023)
- [Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing](https://mlsanity.com/paper/2305.01387/) (2023)
- [Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy](https://mlsanity.com/paper/2405.02341/) (2024)
- [Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping](https://mlsanity.com/paper/2405.18983/) (2024)
- [FedFQ: Federated Learning with Fine-Grained Quantization](https://mlsanity.com/paper/2408.08977/) (2024)
- [Review of Mathematical Optimization in Federated Learning](https://mlsanity.com/paper/2412.01630/) (2024)
- [PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance](https://mlsanity.com/paper/2312.03792/) (2023)
- [FedCAT: Towards Accurate Federated Learning via Device Concatenation](https://mlsanity.com/paper/2202.12751/) (2022)
- [Unlocking the Potential of Federated Learning for Deeper Models](https://mlsanity.com/paper/2306.02701/) (2023)
- [Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization](https://mlsanity.com/paper/2405.17932/) (2024)
- [Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy](https://mlsanity.com/paper/2505.13655/) (2025)
- [Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning](https://mlsanity.com/paper/2410.10833/) (2024)
- [Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks](https://mlsanity.com/paper/2507.19964/) (2025)

## Abstract (arXiv)

Federated learning (FL) enables distributed agents to collaboratively learn a centralized model without sharing their raw data with each other. However, data locality does not provide sufficient privacy protection, and it is desirable to facilitate FL with rigorous differential privacy (DP) guarantee. Existing DP mechanisms would introduce random noise with magnitude proportional to the model size, which can be quite large in deep neural networks. In this paper, we propose a new FL framework with sparsification-amplified privacy. Our approach integrates random sparsification with gradient perturbation on each agent to amplify privacy guarantee. Since sparsification would increase the number of communication rounds required to achieve a certain target accuracy, which is unfavorable for DP guarantee, we further introduce acceleration techniques to help reduce the privacy cost. We rigorously analyze the convergence of our approach and utilize Renyi DP to tightly account the end-to-end DP guarantee. Extensive experiments on benchmark datasets validate that our approach outperforms previous differentially-private FL approaches in both privacy guarantee and communication efficiency.