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.

在分布式协作机器学习领域中，我们提出了一种名为 SFPL 的框架，通过在设备之间分布处理数据并利用正标记数据，使得资源受限的物联网设备在训练深度学习模型时能够达到比现有方法更好的结果。

资源有限的物联网环境中仅包含正标签的联邦分割学习

Federated Split Learning with Only Positive Labels for  resource-constrained IoT environment

We consider learning a multi-class classification model in the federated
setting, where each user has access to the positive data associated with only a
single class. As a result, during each federated learning round, the users need
to locally update the classifier without having access to the features and the
model parameters for the negative classes. Thus, naively employing conventional
decentralized learning such as the distributed SGD or Federated Averaging may
lead to trivial or extremely poor classifiers. In particular, for the embedding
based classifiers, all the class embeddings might collapse to a single point.
To address this problem, we propose a generic framework for training with
only positive labels, namely Federated Averaging with Spreadout (FedAwS), where
the server imposes a geometric regularizer after each round to encourage
classes to be spreadout in the embedding space. We show, both theoretically and
empirically, that FedAwS can almost match the performance of conventional
learning where users have access to negative labels. We further extend the
proposed method to the settings with large output spaces.

本文提出了一个通用的培训框架 FedaWS，它保证了类别在嵌入空间中分散，从而可以学习具有仅正标签的多类分类模型。这个框架可以几乎达到具有负标签访问权限的传统学习的性能。