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A Multi-Prototype-Guided Federated Knowledge Distillation Approach in AI-RAN Enabled Multi-Access Edge Computing System

arXiv 2603.09727 ↗First posted 10 Mar 2026cs.LGcited by 0

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Abstract (arXiv)

With the development of wireless network, Multi-Access Edge Computing (MEC) and Artificial Intelligence (AI)-native Radio Access Network (RAN) have attracted significant attention. Particularly, the integration of AI-RAN and MEC is envisioned to transform network efficiency and responsiveness. Therefore, it is valuable to investigate AI-RAN enabled MEC system. Federated learning (FL) nowadays is emerging as a promising approach for AI-RAN enabled MEC system, in which edge devices are enabled to train a global model cooperatively without revealing their raw data. However, conventional FL encounters the challenge in processing the non-independent and identically distributed (non-IID) data. Single prototype obtained by averaging the embedding vectors per class can be employed in FL to handle the data heterogeneity issue. Nevertheless, this may result in the loss of useful information owing to the average operation. Therefore, in this paper, a multi-prototype-guided federated knowledge distillation (MP-FedKD) approach is proposed. Particularly, self-knowledge distillation is integrated into FL to deal with the non-IID issue. To cope with the problem of information loss caused by single prototype-based strategy, multi-prototype strategy is adopted, where we present a conditional hierarchical agglomerative clustering (CHAC) approach and a prototype alignment scheme. Additionally, we design a novel loss function (called LEMGP loss) for each local client, where the relationship between global prototypes and local embedding will be focused. Extensive experiments over multiple datasets with various non-IID settings showcase that the proposed MP-FedKD approach outperforms the considered state-of-the-art baselines regarding accuracy, average accuracy and errors (RMSE and MAE).

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References · 29

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17 more
  1. 2021Self-Guided and Cross-Guided Learning for Few-Shot Segmentationcited by 92
  2. 2014Convolutional Neural Networks at Constrained Time Costcited by 81
  3. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 80
  4. 2021Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillationcited by 46
  5. 2024FedGTA: Topology-aware Averaging for Federated Graph Learningcited by 44
  6. 2024Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domainscited by 27
  7. 2022Self-Knowledge Distillation via Dropoutcited by 18
  8. 2023FedICT: Federated Multi-task Distillation for Multi-access Edge Computingcited by 17
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  10. 2023Towards Fair Graph Federated Learning via Incentive Mechanismscited by 8
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  14. 2024Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approachcited by 4
  15. 2025The Interplay of AI-and-RAN: Dynamic Resource Allocation for Converged 6G Platformcited by 3
  16. 2018Clustering-Oriented Representation Learning with Attractive-Repulsive Losscited by 2
  17. 2024Cyber Attacks Prevention Towards Prosumer-based EV Charging Stations: An Edge-assisted Federated Prototype Knowledge Distillation Approachcited by 2

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