Recent advances in wearable devices and Internet of Things (IoT) have led to massive growth in sensor data generated in edge devices. Labeling such massive data for classification tasks has proven to be challenging. In addition, data generated by different users bear various personal attributes and edge heterogeneity, rendering it impractical to develop a global model that adapts well to all users. Concerns over data privacy and communication costs also prohibit centralized data accumulation and training. We propose SemiPFL that supports edge users having no label or limited labeled data sets and a sizable amount of unlabeled data that is insufficient to train a well-performing model. In this work, edge users collaborate to train a Hyper-network in the server, generating personalized autoencoders for each user. After receiving updates from edge users, the server produces a set of base models for each user, which the users locally aggregate them using their own labeled data set. We comprehensively evaluate our proposed framework on various public data sets from a wide range of application scenarios, from wearable health to IoT, and demonstrate that SemiPFL outperforms state-of-the-art federated learning frameworks under the same assumptions regarding user performance, network footprint, and computational consumption. We also show that the solution performs well for users without label or having limited labeled data sets and increasing performance for increased labeled data and number of users, signifying the effectiveness of SemiPFL for handling data heterogeneity and limited annotation. We also demonstrate the stability of SemiPFL for handling user hardware resource heterogeneity in three real-time scenarios.

中文翻译：

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SemiPFL：边缘智能的个性化半监督联邦学习框架

可穿戴设备和物联网 (IoT) 的最新进展导致边缘设备中生成的传感器数据大幅增长。为分类任务标记如此大量的数据已被证明具有挑战性。此外，不同用户生成的数据具有不同的个人属性和边缘异构性，使得开发一个能够很好地适应所有用户的全局模型不切实际。对数据隐私和通信成本的担忧也阻碍了集中式数据积累和培训。我们提出的 SemiPFL 支持没有标签或有限标签数据集的边缘用户，以及大量不足以训练性能良好的模型的未标签数据。在这项工作中，边缘用户协作在服务器中训练超网络，为每个用户生成个性化的自动编码器。在收到来自边缘用户的更新后，服务器为每个用户生成一组基本模型，用户使用自己的标记数据集在本地聚合它们。我们在从可穿戴健康到物联网等广泛应用场景的各种公共数据集上综合评估我们提出的框架，并证明在用户性能、网络足迹等相同假设下，SemiPFL 优于最先进的联邦学习框架和计算消耗。我们还表明，该解决方案对于没有标签或标签数据集有限的用户表现良好，并且随着标签数据和用户数量的增加而提高性能，这表明 SemiPFL 在处理数据异质性和有限注释方面的有效性。