mlsanityData to 14 Oct 2026

Local Differential Privacy based Federated Learning for Internet of Things

arXiv 2004.08856 ↗First posted 19 Apr 2020cs.CR cs.LGcited by 75

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 19; 2023: 19; 2024: 11; 2025: 13; 2026: 4.

75 papers.

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  1. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  2. 2021Differentially Private Federated Learning on Heterogeneous Datacited by 74
  3. 2022No Free Lunch Theorem for Security and Utility in Federated Learningcited by 39
  4. 2022Joint Privacy Enhancement and Quantization in Federated Learningcited by 35
  5. 2022SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compressioncited by 34
  6. 2021Differentially Private Federated Bayesian Optimization with Distributed Explorationcited by 32
  7. 2021Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Lossescited by 30
  8. 2022Private Non-Convex Federated Learning Without a Trusted Servercited by 26
  9. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  10. 2023Active Membership Inference Attack under Local Differential Privacy in Federated Learningcited by 17
  11. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
  12. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 15
38 more
  1. 2022Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clusteringcited by 14
  2. 2023Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learningcited by 14
  3. 2024FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneitycited by 13
  4. 2022XRand: Differentially Private Defense against Explanation-Guided Attackscited by 11
  5. 2023Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Thingscited by 10
  6. 2021FL-Market: Trading Private Models in Federated Learningcited by 9
  7. 2023Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approachcited by 9
  8. 2024Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learningcited by 9
  9. 2023Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networkscited by 7
  10. 2025Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Associationcited by 7
  11. 2021FLSys: Toward an Open Ecosystem for Federated Learning Mobile Appscited by 6
  12. 2022Group privacy for personalized federated learningcited by 5
  13. 2022Asynchronous Federated Learning for Edge-assisted Vehicular Networkscited by 5
  14. 2022Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networkscited by 5
  15. 2023Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computingcited by 5
  16. 2024PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloudcited by 5
  17. 2025Experimentally validated quantum-secure federated learning over a multi-user quantum networkcited by 5
  18. 2022OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsificationcited by 4
  19. 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
  20. 2022Hierarchical Federated Learning with Privacycited by 4
  21. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  22. 2024Differentially Private Clustered Federated Learningcited by 4
  23. 2021Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffscited by 3
  24. 2022Federated clustering with GAN-based data synthesiscited by 3
  25. 2023FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Userscited by 3
  26. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  27. 2024Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectivescited by 3
  28. 2021DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identitiescited by 2
  29. 2021Utility Fairness for the Differentially Private Federated Learningcited by 2
  30. 2022Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networkscited by 2
  31. 2023Personalized DP-SGD using Sampling Mechanismscited by 2
  32. 2023Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyondcited by 2
  33. 2025Information-Theoretic Decentralized Secure Aggregation with Passive Collusion Resiliencecited by 2
  34. 2025Federated Learning of Quantile Inference under Local Differential Privacycited by 2
  35. 2025FedMPDD: Communication-Efficient Federated Learning with Privacy Preservation Attributes via Projected Directional Derivativecited by 2
  36. 2022Federated Learning Cost Disparity for IoT Devicescited by 1
  37. 2022Optimal Privacy Preserving for Federated Learning in Mobile Edge Computingcited by 1
  38. 2023Balancing Privacy and Performance for Private Federated Learning Algorithmscited by 1

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