mlsanityData to 14 Oct 2026

Exploiting Label Skewness for Spiking Neural Networks in Federated Learning

arXiv 2412.17305 ↗First posted 23 Dec 2024cs.LG cs.CVcited by 3

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Abstract (arXiv)

The energy efficiency of deep spiking neural networks (SNNs) aligns with the constraints of resource-limited edge devices, positioning SNNs as a promising foundation for intelligent applications leveraging the extensive data collected by these devices. To address data privacy concerns when deploying SNNs on edge devices, federated learning (FL) facilitates collaborative model training by leveraging data distributed across edge devices without transmitting local data to a central server. However, existing FL approaches struggle with label-skewed data across devices, which leads to drift in local SNN models and degrades the performance of the global SNN model. In this paper, we propose a novel framework called FedLEC, which incorporates intra-client label weight calibration to balance the learning intensity across local labels and inter-client knowledge distillation to mitigate local SNN model bias caused by label absence. Extensive experiments with three different structured SNNs across five datasets (i.e., three non-neuromorphic and two neuromorphic datasets) demonstrate the efficiency of FedLEC. Compared to eight state-of-the-art FL algorithms, FedLEC achieves an average accuracy improvement of approximately 11.59% for the global SNN model under various label skew distribution settings.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 35 references that are in the map. First posted on arXiv 23 Dec 2024.

Builds on

17 of 35 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  5. 2020Long-tail learning via logit adjustmentcited by 618
  6. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  7. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
  8. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 314
  9. 2018Direct Training for Spiking Neural Networks: Faster, Larger, Bettercited by 233
  10. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 109
  11. 2024Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chipscited by 103
  12. 2021Advancing Spiking Neural Networks towards Deep Residual Learningcited by 79
5 more
  1. 2023Deep Directly-Trained Spiking Neural Networks for Object Detectioncited by 77
  2. 2023Exploiting Label Skews in Federated Learning with Model Concatenationcited by 14
  3. 2022Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognitioncited by 12
  4. 2024Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networkscited by 9
  5. 2023FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentationcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Edge Intelligence with Spiking Neural Networkscited by 2
  2. 2025SAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Networkcited by 0
  3. 2026AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Exploring Vacant Classes in Label-Skewed Federated Learning
  2. 2024Time-Distributed Backdoor Attacks on Federated Spiking Learning
  3. 2025Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment
  4. 2022FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers
  5. 2021Energy-efficient Knowledge Distillation for Spiking Neural Networks
  6. 2022SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
  7. 2022Towards Federated Learning against Noisy Labels via Local Self-Regularization
  8. 2021FedHe: Heterogeneous Models and Communication-Efficient Federated Learning