Federated learning-assisted edge intelligence enables privacy protection in modern intelligent services. However, not Independent and Identically Distributed (non-IID) distribution among edge clients can impair the local model performance. The existing single prototype-based strategy represents a sample by using the mean of the feature space. However, feature spaces are usually not clustered, and a single prototype may not represent a sample well. Motivated by this, this paper proposes a multi-prototype federated contrastive learning approach (MP-FedCL) which demonstrates the effectiveness of using a multi-prototype strategy over a single-prototype under non-IID settings, including both label and feature skewness. Specifically, a multi-prototype computation strategy based on \textit{k-means} is first proposed to capture different embedding representations for each class space, using multiple prototypes ($k$ centroids) to represent a class in the embedding space. In each global round, the computed multiple prototypes and their respective model parameters are sent to the edge server for aggregation into a global prototype pool, which is then sent back to all clients to guide their local training. Finally, local training for each client minimizes their own supervised learning tasks and learns from shared prototypes in the global prototype pool through supervised contrastive learning, which encourages them to learn knowledge related to their own class from others and reduces the absorption of unrelated knowledge in each global iteration. Experimental results on MNIST, Digit-5, Office-10, and DomainNet show that our method outperforms multiple baselines, with an average test accuracy improvement of about 4.6\% and 10.4\% under feature and label non-IID distributions, respectively.

翻译：联邦学习辅助的边缘智能技术实现了现代智能服务中的隐私保护。然而，边缘客户端间的非独立同分布（non-IID）会削弱本地模型性能。现有基于单一原型的方法通过特征空间的均值表示样本，但特征空间通常不呈簇状分布，单一原型难以有效表征样本。受此启发，本文提出一种多原型联邦对比学习方法（MP-FedCL），证明了在标签偏移与特征偏移的non-IID场景下，多原型策略相比单原型策略的有效性。具体而言：首先提出基于_k-means_的多原型计算策略，通过多原型（$k$个质心）表征每个类别空间中的嵌入表示，捕获不同类别空间的多样性特征。在每个全局轮次中，计算得到的多原型及其对应模型参数被发送至边缘服务器，聚合形成全局原型池后分发回所有客户端以指导本地训练。最终，各客户端通过最小化自身监督学习任务并借助全局原型池中的共享原型执行监督对比学习，在全局迭代中激励模型吸收与自身类别相关的知识，同时减少无关知识的干扰。在MNIST、Digit-5、Office-10和DomainNet数据集上的实验表明，本方法在特征non-IID和标签non-IID分布下分别实现平均测试准确率提升约4.6%和10.4%，显著优于多个基线方法。