{"type":"ScholarlyArticle","title":"Exploiting Label Skewness for Spiking Neural Networks in Federated Learning","authors":[{"name":"Di Yu","slug":"di-yu","given":"Di","family":"Yu","affiliation":""},{"name":"Xin Du","slug":"xin-du","given":"Xin","family":"Du","affiliation":""},{"name":"Linshan Jiang","slug":"linshan-jiang","given":"Linshan","family":"Jiang","affiliation":""},{"name":"Huijing Zhang","slug":"huijing-zhang","given":"Huijing","family":"Zhang","affiliation":""},{"name":"Shuiguang Deng","slug":"shuiguang-deng","given":"Shuiguang","family":"Deng","affiliation":""}],"abstract":"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 safeguard data privacy, federated learning (FL) facilitates collaborative SNN-based model training by leveraging data distributed across edge devices without transmitting local data to a central server. However, existing FL approaches encounter challenges in handling label-skewed data across devices, inducing drift in the local SNN model and consequently impairing the performance of the global SNN model. To tackle these problems, 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 seven 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.","doi":"10.24963/ijcai.2025/767","url":"https://ijcai.org/proceedings/2025/767","pdf":"https://ijcai.org/proceedings/2025/0767.pdf","markdown":"https://ijcai.org/proceedings/2025/767.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/767","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":767,"pages":{"first":6895,"last":6903},"keywords":["Computer Vision: CV: Applications and Systems","Machine Learning: ML: Federated learning","Data Mining: DM: Class imbalance and unequal cost","Humans and AI: HAI: Brain sciences"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}