Clustered Federated Multitask Learning (CFL) has gained considerable
attention as an effective strategy for overcoming statistical challenges,
particularly when dealing with non independent and identically distributed (non
IID) data across multiple users. However, much of the existing research on CFL
operates under the unrealistic premise that devices have access to accurate
ground truth labels. This assumption becomes especially problematic in
hierarchical wireless networks (HWNs), where edge networks contain a large
amount of unlabeled data, resulting in slower convergence rates and increased
processing times, particularly when dealing with two layers of model
aggregation. To address these issues, we introduce a novel framework, Clustered
Federated Semi-Supervised Learning (CFSL), designed for more realistic HWN
scenarios. Our approach leverages a best-performing specialized model
algorithm, wherein each device is assigned a specialized model that is highly
adept at generating accurate pseudo-labels for unlabeled data, even when the
data stems from diverse environments. We validate the efficacy of CFSL through
extensive experiments, comparing it with existing methods highlighted in recent
literature. Our numerical results demonstrate that CFSL significantly improves
upon key metrics such as testing accuracy, labeling accuracy, and labeling
latency under varying proportions of labeled and unlabeled data while also
accommodating the non-IID nature of the data and the unique characteristics of
wireless edge networks.

在集群联邦多任务学习 (CFL) 中，针对多个用户间的非独立同分布 (non-IID) 数据的统计挑战，Clustered Federated Semi-Supervised Learning (CFSL) 应用于更真实的分层无线网络 (HWN) 场景，通过引入适用于生成准确伪标签的专门模型算法，显著改善了测试准确性、标注准确性和标注延迟等关键指标，以适应数据的非独立同分布性质和无线边缘网络的独特特点。