mlsanityData to 14 Oct 2026

Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

arXiv 2205.14315 ↗First posted 28 May 2022cs.LG eess.SPcited by 12

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Abstract (arXiv)

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 23 of its 59 references that are in the map. First posted on arXiv 28 May 2022.

Builds on

23 of 59 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2015Spatial Transformer Networkscited by 1,429
  3. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  4. 2017Understanding the Effective Receptive Field in Deep Convolutional Neural Networkscited by 553
  5. 2017Deep Interest Network for Click-Through Rate Predictioncited by 544
  6. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
  7. 2018How Does Batch Normalization Help Optimization?cited by 357
  8. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 245
  9. 2018Direct Training for Spiking Neural Networks: Faster, Larger, Bettercited by 233
  10. 2017Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networkscited by 126
  11. 2019Reliable Federated Learning for Mobile Networkscited by 92
  12. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 83
11 more
  1. 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 80
  2. 2019Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagationcited by 45
  3. 2021Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium Statecited by 40
  4. 2019Deep Learning for Large-Scale Traffic-Sign Detection and Recognitioncited by 34
  5. 2021Federated Learning with Spiking Neural Networkscited by 23
  6. 2019Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligencecited by 16
  7. 2021FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computingcited by 11
  8. 2021An Adaptive Sampling and Edge Detection Approach for Encoding Static Images for Spiking Neural Networkscited by 4
  9. 2021Secure and Reliable Transfer Learning Framework for 6G-enabled Internet of Vehiclescited by 4
  10. 2020Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehiclecited by 3
  11. 2015Spatial Spectrum and Energy Efficiency of Random Cellular Networkscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 5; 2025: 1; 2026: 1.

12 papers.

  1. 2023Federated Learning for Computer Visioncited by 22
  2. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
  3. 2023FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systemscited by 14
  4. 2024Exploiting Label Skewness for Spiking Neural Networks in Federated Learningcited by 3
  5. 2024Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devicescited by 2
  6. 2024FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehiclescited by 2
  7. 2023Communication Trade-offs in Federated Learning of Spiking Neural Networkscited by 1
  8. 2023FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Predictioncited by 1
  9. 2024Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoningcited by 1
  10. 2024The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learningcited by 1
  11. 2025Secure Generalization through Stochastic Bidirectional Parameter Updates Using Dual-Gradient Mechanismcited by 0
  12. 2026QDS-SNN: Energy-efficient Quantum Deeply-Supervised Spiking Neural Network Algorithm for Traffic Sign Recognitioncited by 0