mlsanityData to 14 Oct 2026

Federated Learning with Spiking Neural Networks

arXiv 2106.06579 ↗First posted 11 Jun 2021cs.LG cs.CV cs.DCcited by 23

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Abstract (arXiv)

As neural networks get widespread adoption in resource-constrained embedded devices, there is a growing need for low-power neural systems. Spiking Neural Networks (SNNs)are emerging to be an energy-efficient alternative to the traditional Artificial Neural Networks (ANNs) which are known to be computationally intensive. From an application perspective, as federated learning involves multiple energy-constrained devices, there is a huge scope to leverage energy efficiency provided by SNNs. Despite its importance, there has been little attention on training SNNs on a large-scale distributed system like federated learning. In this paper, we bring SNNs to a more realistic federated learning scenario. Specifically, we propose a federated learning framework for decentralized and privacy-preserving training of SNNs. To validate the proposed federated learning framework, we experimentally evaluate the advantages of SNNs on various aspects of federated learning with CIFAR10 and CIFAR100 benchmarks. We observe that SNNs outperform ANNs in terms of overall accuracy by over 15% when the data is distributed across a large number of clients in the federation while providing up to5.3x energy efficiency. In addition to efficiency, we also analyze the sensitivity of the proposed federated SNN framework to data distribution among the clients, stragglers, and gradient noise and perform a comprehensive comparison with ANNs.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 28 of its 45 references that are in the map. First posted on arXiv 11 Jun 2021.

Builds on

28 of 45 references.

References · 28

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2018Federated Learning with Non-IID Datacited by 1,108
  6. 2019Deep Leakage from Gradientscited by 1,021
  7. 2017Federated Multi-Task Learningcited by 672
  8. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 626
  9. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  10. 2020Federated Learning with Matched Averagingcited by 564
  11. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  12. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
16 more
  1. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 406
  2. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
  3. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 312
  4. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 246
  5. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 245
  6. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 144
  7. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  8. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 107
  9. 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 80
  10. 2020Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activationscited by 48
  11. 2016Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardwarecited by 40
  12. 2020Optimal Gradient Compression for Distributed and Federated Learningcited by 39
  13. 2021Visual Explanations from Spiking Neural Networks using Interspike Intervalscited by 27
  14. 2019Low-Power Neuromorphic Hardware for Signal Processing Applicationscited by 21
  15. 2019Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligencecited by 16
  16. 2016Training Spiking Deep Networks for Neuromorphic Hardwarecited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 4; 2024: 7; 2025: 2; 2026: 4.

23 papers.

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  1. 2021Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentationcited by 55
  2. 2022Neural Architecture Search for Spiking Neural Networkscited by 53
  3. 2023Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANNcited by 46
  4. 2022Exploring Lottery Ticket Hypothesis in Spiking Neural Networkscited by 26
  5. 2023Green Edge AI: A Contemporary Surveycited by 25
  6. 2021Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networkscited by 23
  7. 2022Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognitioncited by 12
  8. 2024Time-Distributed Backdoor Attacks on Federated Spiking Learningcited by 4
  9. 2023Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technologycited by 3
  10. 2024Neuromorphic Wireless Split Computing with Multi-Level Spikescited by 3
  11. 2022STSyn: Speeding Up Local SGD with Straggler-Tolerant Synchronizationcited by 2
  12. 2024Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devicescited by 2
11 more
  1. 2024SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learningcited by 2
  2. 2025Edge Intelligence with Spiking Neural Networkscited by 2
  3. 2023Communication Trade-offs in Federated Learning of Spiking Neural Networkscited by 1
  4. 2024The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learningcited by 1
  5. 2025Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Datacited by 1
  6. 2024Spiking Neural Networks in Vertical Federated Learning: Performance Trade-offscited by 0
  7. 2024Emulating Brain-like Rapid Learning in Neuromorphic Edge Computingcited by 0
  8. 2026On the Sensitivity of Firing Rate-Based Federated Spiking Neural Networks to Differential Privacycited by 0
  9. 2026Federated Few-Shot Learning on Neuromorphic Hardware: An Empirical Study Across Physical Edge Nodescited by 0
  10. 2026Spiking Personalized Federated Learning for Brain-Computer Interface-Enabled Immersive Communicationcited by 0
  11. 2026Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutionscited by 0