Distributed collaborative machine learning (DCML) is a promising method in the Internet of Things (IoT) domain for training deep learning models, as data is distributed across multiple devices. A key advantage of this approach is that it improves data privacy by removing the necessity for the centralized aggregation of raw data but also empowers IoT devices with low computational power. Among various techniques in a DCML framework, federated split learning, known as splitfed learning (SFL), is the most suitable for efficient training and testing when devices have limited computational capabilities. Nevertheless, when resource-constrained IoT devices have only positive labeled data, multiclass classification deep learning models in SFL fail to converge or provide suboptimal results. To overcome these challenges, we propose splitfed learning with positive labels (SFPL). SFPL applies a random shuffling function to the smashed data received from clients before supplying it to the server for model training. Additionally, SFPL incorporates the local batch normalization for the client-side model portion during the inference phase. Our results demonstrate that SFPL outperforms SFL: (i) by factors of 51.54 and 32.57 for ResNet-56 and ResNet-32, respectively, with the CIFAR-100 dataset, and (ii) by factors of 9.23 and 8.52 for ResNet-32 and ResNet-8, respectively, with CIFAR-10 dataset. Overall, this investigation underscores the efficacy of the proposed SFPL framework in DCML.

翻译：分布式协作机器学习（DCML）是物联网领域中训练深度学习模型的一种有前景的方法，因为数据分布在多个设备上。该方法的关键优势在于，它通过消除原始数据集中聚合的必要性来提升数据隐私，同时还能赋能计算能力较低的物联网设备。在DCML框架的多种技术中，当设备计算能力有限时，联邦分割学习（称为分割联邦学习，SFL）是最适合高效训练和测试的技术。然而，当资源受限的物联网设备仅拥有正标签数据时，SFL中的多分类深度学习模型无法收敛或仅能提供次优结果。为克服这些挑战，我们提出了仅正标签分割联邦学习（SFPL）。SFPL在将接收自客户端的压缩数据传输至服务器进行模型训练前，对其应用随机洗牌函数。此外，SFPL在推理阶段对客户端侧模型部分引入局部批归一化。实验结果表明，SFPL优于SFL：（i）在使用CIFAR-100数据集时，ResNet-56和ResNet-32分别提升51.54倍和32.57倍；（ii）在使用CIFAR-10数据集时，ResNet-32和ResNet-8分别提升9.23倍和8.52倍。总体而言，本研究强调了所提出的SFPL框架在DCML中的有效性。