# Local Differential Privacy based Federated Learning for Internet of Things

arXiv: https://arxiv.org/abs/2004.08856 · HTML: https://mlsanity.com/paper/2004.08856/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 30 of 75)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Differentially Private Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2111.09278/) (2021)
- [No Free Lunch Theorem for Security and Utility in Federated Learning](https://mlsanity.com/paper/2203.05816/) (2022)
- [Joint Privacy Enhancement and Quantization in Federated Learning](https://mlsanity.com/paper/2208.10888/) (2022)
- [SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression](https://mlsanity.com/paper/2206.09888/) (2022)
- [Differentially Private Federated Bayesian Optimization with Distributed Exploration](https://mlsanity.com/paper/2110.14153/) (2021)
- [Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses](https://mlsanity.com/paper/2106.09779/) (2021)
- [Private Non-Convex Federated Learning Without a Trusted Server](https://mlsanity.com/paper/2203.06735/) (2022)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [Active Membership Inference Attack under Local Differential Privacy in Federated Learning](https://mlsanity.com/paper/2302.12685/) (2023)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering](https://mlsanity.com/paper/2210.14664/) (2022)
- [Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning](https://mlsanity.com/paper/2303.04274/) (2023)
- [FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity](https://mlsanity.com/paper/2404.09816/) (2024)
- [XRand: Differentially Private Defense against Explanation-Guided Attacks](https://mlsanity.com/paper/2212.04454/) (2022)
- [Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things](https://mlsanity.com/paper/2304.12889/) (2023)
- [FL-Market: Trading Private Models in Federated Learning](https://mlsanity.com/paper/2106.04384/) (2021)
- [Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach](https://mlsanity.com/paper/2311.18498/) (2023)
- [Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning](https://mlsanity.com/paper/2406.03519/) (2024)
- [Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks](https://mlsanity.com/paper/2307.11468/) (2023)
- [Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association](https://mlsanity.com/paper/2503.04564/) (2025)
- [FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps](https://mlsanity.com/paper/2111.09445/) (2021)
- [Group privacy for personalized federated learning](https://mlsanity.com/paper/2206.03396/) (2022)
- [Asynchronous Federated Learning for Edge-assisted Vehicular Networks](https://mlsanity.com/paper/2208.01901/) (2022)
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://mlsanity.com/paper/2208.06963/) (2022)
- [Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing](https://mlsanity.com/paper/2304.02832/) (2023)
- [PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud](https://mlsanity.com/paper/2404.13475/) (2024)
- [Experimentally validated quantum-secure federated learning over a multi-user quantum network](https://mlsanity.com/paper/2501.12709/) (2025)
- [OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification](https://mlsanity.com/paper/2202.07165/) (2022)

## Abstract (arXiv)

Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users' location information, which raises severe location privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The Three-Outputs mechanism is proposed which introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB.