针对边缘计算中数据异构问题，提出多原型引导的联邦知识蒸馏方法。
A Multi-Prototype-Guided Federated Knowledge Distillation Approach in AI-RAN Enabled Multi-Access Edge Computing System
- 用多原型替代单原型，结合聚类与对齐策略减少信息损失。
- 在多个非独立同分布数据集上，准确率与平均准确率均优于现有方法。
- 适合关注联邦学习在异构边缘场景下性能优化的研究者。
随着无线网络的发展，多接入边缘计算（MEC）和人工智能原生无线接入网（AI-RAN）受到广泛关注。尤其，AI-RAN与MEC的融合有望提升网络效率与响应能力。因此，研究基于AI-RAN的MEC系统具有重要意义。当前，联邦学习（FL）作为该系统的有前景方案，使边缘设备可在不共享原始数据的前提下协作训练全局模型。然而，传统联邦学习面临非独立同分布（non-IID）数据的挑战。通过平均每类嵌入向量获得的单原型虽可缓解数据异构问题，但平均操作可能导致有用信息丢失。为此，本文提出一种多原型引导的联邦知识蒸馏（MP-FedKD）方法。特别地，将自知识蒸馏引入联邦学习以应对非独立同分布问题。为解决单原型策略导致的信息损失，采用多原型策略，提出条件分层聚类（CHAC）方法与原型对齐方案。此外，设计了一种新型本地损失函数（称为LEMGP损失），聚焦全局原型与本地嵌入之间的关系。在多个数据集及多种非独立同分布设置下的大量实验表明，所提方法在准确率、平均准确率以及均方根误差（RMSE）和平均绝对误差（MAE）方面均优于现有先进基线。
原文摘要 · Abstract (English)
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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