Recent years have witnessed a huge demand for artificial intelligence and machine learning applications in wireless edge networks to assist individuals with real-time services. Owing to the practical setting and privacy preservation of federated learning (FL), it is a suitable and appealing distributed learning paradigm to deploy these applications at the network edge. Despite the many successful efforts made to apply FL to wireless edge networks, the adopted algorithms mostly follow the same spirit as FedAvg, thereby heavily suffering from the practical challenges of label deficiency and device heterogeneity. These challenges not only decelerate the model training in FL but also downgrade the application performance. In this paper, we focus on the algorithm design and address these challenges by investigating the personalized semi-supervised FL problem and proposing an effective algorithm, namely FedCPSL. In particular, the techniques of pseudo-labeling, and interpolation-based model personalization are judiciously combined to provide a new problem formulation for personalized semi-supervised FL. The proposed FedCPSL algorithm adopts novel strategies, including adaptive client variance reduction, local momentum, and normalized global aggregation, to combat the challenge of device heterogeneity and boost algorithm convergence. Moreover, the convergence property of FedCPSL is thoroughly analyzed and shows that FedCPSL is resilient to both statistical and system heterogeneity, obtaining a sublinear convergence rate. Experimental results on image classification tasks are also presented to demonstrate that the proposed approach outperforms its counterparts in terms of both convergence speed and application performance.

翻译：近年来，无线边缘网络中人工智能与机器学习应用的巨大需求日益凸显，以支持实时个性化服务。鉴于联邦学习的实际部署场景与隐私保护特性，它是一种适用于网络边缘部署上述应用的分布式学习范式。尽管在无线边缘网络应用联邦学习方面已取得诸多成功尝试，但所采用的算法大多遵循FedAvg的核心理念，因而严重受限于标签不足与设备异构性等实际挑战。这些挑战不仅降低了联邦学习中的模型训练速度，也削弱了应用性能。本文聚焦算法设计，通过研究个性化半监督联邦学习问题并提出一种高效算法FedCPSL来应对上述挑战。具体而言，我们巧妙结合伪标签技术与基于插值的模型个性化方法，提出了个性化半监督联邦学习的新问题表述。所提出的FedCPSL算法采用自适应客户端方差缩减、局部动量与归一化全局聚合等创新策略，以克服设备异构性挑战并加速算法收敛。此外，本文全面分析了FedCPSL的收敛特性，证明其对统计异构性与系统异构性均具有鲁棒性，可实现亚线性收敛速率。图像分类任务的实验结果进一步表明，所提方法在收敛速度与应用性能方面均优于现有对比方法。