# Asynchronous Federated Learning with Differential Privacy for Edge Intelligence

arXiv: https://arxiv.org/abs/1912.07902 · HTML: https://mlsanity.com/paper/1912.07902/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 16 of 16)

- [CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2112.08909/) (2021)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2210.01161/) (2022)
- [Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data](https://mlsanity.com/paper/2203.01214/) (2022)
- [PersA-FL: Personalized Asynchronous Federated Learning](https://mlsanity.com/paper/2210.01176/) (2022)
- [Coding for Straggler Mitigation in Federated Learning](https://mlsanity.com/paper/2109.15226/) (2021)
- [PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds](https://mlsanity.com/paper/2405.02638/) (2024)
- [Combating Client Dropout in Federated Learning via Friend Model Substitution](https://mlsanity.com/paper/2205.13222/) (2022)
- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy](https://mlsanity.com/paper/2110.11646/) (2021)
- [Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems](https://mlsanity.com/paper/2401.13366/) (2024)
- [DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication](https://mlsanity.com/paper/2512.13583/) (2025)
- [Federated Learning based on Pruning and Recovery](https://mlsanity.com/paper/2403.15439/) (2024)
- [P4: Towards private, personalized, and Peer-to-Peer learning](https://mlsanity.com/paper/2405.17697/) (2024)
- [Dyn-D2P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee](https://mlsanity.com/paper/2505.06651/) (2025)
- [Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning](https://mlsanity.com/paper/2506.20413/) (2025)

## Abstract (arXiv)

Federated learning has been showing as a promising approach in paving the last mile of artificial intelligence, due to its great potential of solving the data isolation problem in large scale machine learning. Particularly, with consideration of the heterogeneity in practical edge computing systems, asynchronous edge-cloud collaboration based federated learning can further improve the learning efficiency by significantly reducing the straggler effect. Despite no raw data sharing, the open architecture and extensive collaborations of asynchronous federated learning (AFL) still give some malicious participants great opportunities to infer other parties' training data, thus leading to serious concerns of privacy. To achieve a rigorous privacy guarantee with high utility, we investigate to secure asynchronous edge-cloud collaborative federated learning with differential privacy, focusing on the impacts of differential privacy on model convergence of AFL. Formally, we give the first analysis on the model convergence of AFL under DP and propose a multi-stage adjustable private algorithm (MAPA) to improve the trade-off between model utility and privacy by dynamically adjusting both the noise scale and the learning rate. Through extensive simulations and real-world experiments with an edge-could testbed, we demonstrate that MAPA significantly improves both the model accuracy and convergence speed with sufficient privacy guarantee.