mlsanityData to 7 Oct 2026

Federated Learning for Ultra-Reliable Low-Latency V2V Communications

arXiv 1805.09253 ↗First posted 11 May 2018cs.NI cs.LG stat.MLcited by 30

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Abstract (arXiv)

In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilistic queuing delays. In particular, a reliability measure is defined to characterize extreme events (i.e., when vehicles' queue lengths exceed a predefined threshold with non-negligible probability) using extreme value theory (EVT). Leveraging principles from federated learning (FL), the distribution of these extreme events corresponding to the tail distribution of queues is estimated by VUEs in a decentralized manner. Finally, Lyapunov optimization is used to find the joint transmit power and resource allocation policies for each VUE in a distributed manner. The proposed solution is validated via extensive simulations using a Manhattan mobility model. It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79% reductions in the amount of data that need to be exchanged. Furthermore, the proposed method yields up to 60% reductions of VUEs with large queue lengths, without an additional power consumption, compared to an average queue-based baseline. Compared to systems with fixed power consumption and focusing on queue stability while minimizing average power consumption, the reduction in extreme events of the proposed method is about two orders of magnitude.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 May 2018.

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Citing papers per year: 2021: 16; 2022: 4; 2023: 5; 2024: 4; 2025: 1.

30 papers.

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  1. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 320
  2. 2021A Field Guide to Federated Optimizationcited by 312
  3. 2022Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communicationcited by 47
  4. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 39
  5. 2022FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learningcited by 37
  6. 2021Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunitiescited by 22
  7. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  8. 2021FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systemscited by 16
  9. 2021Real-time End-to-End Federated Learning: An Automotive Case Studycited by 15
  10. 2022Deep Learning Serves Traffic Safety Analysis: A Forward-looking Reviewcited by 13
  11. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  12. 2021Failure Prediction in Production Line Based on Federated Learning: An Empirical Studycited by 11
18 more
  1. 2021Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updatescited by 10
  2. 2021FLRA: A Reference Architecture for Federated Learning Systemscited by 9
  3. 2021Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehiclescited by 7
  4. 2021Machine Learning for Security in Vehicular Networks: A Comprehensive Surveycited by 7
  5. 2023Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoTcited by 6
  6. 2022Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sourcescited by 5
  7. 2025CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Samplingcited by 5
  8. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  9. 2021Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systemscited by 3
  10. 2023Federated Reinforcement Learning for Resource Allocation in V2X Networkscited by 3
  11. 2024Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learningcited by 2
  12. 2021SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systemscited by 1
  13. 2024Noise-Robust and Resource-Efficient ADMM-based Federated Learningcited by 1
  14. 2021Multi-task Federated Edge Learning (MtFEEL) in Wireless Networkscited by 0
  15. 2021Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decodingcited by 0
  16. 2023RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligencecited by 0
  17. 2024Personalized Federated Learning for Statistical Heterogeneitycited by 0
  18. 2024Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchaincited by 0