Smartphones, wearables, and Internet-of-Things (IoT) devices produce a wealth of data that cannot be accumulated in a centralized repository for learning supervised models due to privacy, bandwidth limitations, and the prohibitive cost of annotations. Federated learning provides a compelling framework for learning models from decentralized data, but conventionally, it assumes the availability of labeled samples, whereas on-device data are generally either unlabeled or cannot be annotated readily through user interaction. To address these issues, we propose a self-supervised approach termed scalogram-signal correspondence learning based on wavelet transform (WT) to learn useful representations from unlabeled sensor inputs as electroencephalography, blood volume pulse, accelerometer, and WiFi channel-state information. Our auxiliary task requires a deep temporal neural network to determine if a given pair of a signal and its complementary view (i.e., a scalogram generated with WT) align with each other, by optimizing a contrastive objective. We extensively assess the quality of learned features with our multiview strategy on diverse public data sets, achieving strong performance in all domains. We demonstrate the effectiveness of representations learned from an unlabeled input collection on downstream tasks with training a linear classifier over pretrained network, usefulness in low-data regime, transfer learning, and cross-validation. Our methodology achieves competitive performance with fully supervised networks and it works significantly better than pretraining with autoencoders in both central and federated contexts. Notably, it improves the generalization in a semisupervised setting as it reduces the volume of labeled data required through leveraging self-supervised learning.

中文翻译：

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嵌入式智能多传感器表示的联合自监督学习


智能手机、可穿戴设备和物联网 (IoT) 设备产生大量数据，由于隐私、带宽限制和注释成本高昂，这些数据无法积累在用于学习监督模型的集中存储库中。联邦学习为从分散数据中学习模型提供了一个引人注目的框架，但传统上，它假设标记样本的可用性，而设备上的数据通常是未标记的或无法通过用户交互轻松注释。为了解决这些问题，我们提出了一种基于小波变换（WT）的自我监督方法，称为尺度图信号对应学习，从未标记的传感器输入中学习有用的表示，如脑电图、血容量脉冲、加速度计和 WiFi 通道状态信息。我们的辅助任务需要一个深度时间神经网络，通过优化对比目标来确定给定的信号对及其互补视图（即使用小波变换生成的尺度图）是否彼此对齐。我们通过对不同公共数据集的多视图策略广泛评估学习特征的质量，在所有领域都取得了出色的表现。我们通过在预训练网络上训练线性分类器、在低数据状态、迁移学习和交叉验证中的有用性，展示了从下游任务中未标记的输入集合中学习到的表示的有效性。我们的方法通过完全监督的网络实现了具有竞争力的性能，并且在中央和联合上下文中它的效果明显优于使用自动编码器进行预训练。 值得注意的是，它提高了半监督环境中的泛化能力，因为它通过利用自监督学习减少了所需的标记数据量。