Motion sensors integrated into wearable and mobile devices provide valuable
information about the device users. Machine learning and, recently, deep
learning techniques have been used to characterize sensor data. Mostly, a
single task, such as recognition of activities, is targeted, and the data is
processed centrally at a server or in a cloud environment. However, the same
sensor data can be utilized for multiple tasks and distributed machine-learning
techniques can be used without the requirement of the transmission of data to a
centre. This paper explores Federated Transfer Learning in a Multi-Task manner
for both sensor-based human activity recognition and device position
identification tasks. The OpenHAR framework is used to train the models, which
contains ten smaller datasets. The aim is to obtain model(s) applicable for
both tasks in different datasets, which may include only some label types.
Multiple experiments are carried in the Flower federated learning environment
using the DeepConvLSTM architecture. Results are presented for federated and
centralized versions under different parameters and restrictions. By utilizing
transfer learning and training a task-specific and personalized federated
model, we obtained a similar accuracy with training each client individually
and higher accuracy than a fully centralized approach.

该研究论文探讨了在多任务环境下的联邦迁移学习，用于基于传感器的人体活动识别和设备位置识别任务，并通过使用 OpenHAR 框架和 DeepConvLSTM 结构在联邦学习环境中进行了多个实验，结果显示相比个别客户端训练和完全集中化方法，利用迁移学习和训练的任务特定及个性化的联邦模型可以获得类似的准确性并且更高的准确性。

FedOpenHAR：传感器基于的人体活动识别的联邦多任务迁移学习

FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based  Human Activity Recognition

Human Activity Recognition (HAR) based on motion sensors has drawn a lot of
attention over the last few years, since perceiving the human status enables
context-aware applications to adapt their services on users' needs. However,
motion sensor fusion and feature extraction have not reached their full
potentials, remaining still an open issue. In this paper, we introduce
PerceptionNet, a deep Convolutional Neural Network (CNN) that applies a late 2D
convolution to multimodal time-series sensor data, in order to extract
automatically efficient features for HAR. We evaluate our approach on two
public available HAR datasets to demonstrate that the proposed model fuses
effectively multimodal sensors and improves the performance of HAR. In
particular, PerceptionNet surpasses the performance of state-of-the-art HAR
methods based on: (i) features extracted from humans, (ii) deep CNNs exploiting
early fusion approaches, and (iii) Long Short-Term Memory (LSTM), by an average
accuracy of more than 3%.

本文介绍了一种基于深度卷积神经网络的感知网络（PerceptionNet），其应用于多模态时间序列传感器数据中的后期二维卷积，以自动提取用于人类动作识别的有效特征。我们通过在两个公共可用的 HAR 数据集上进行评估来展示我们的方法，表明所提出的模型有效地融合了多模态传感器，并通过超过 3％的平均准确度优于基于：（i）从人体中提取的特征，（ii）利用早期融合方法的深度卷积神经网络和（iii）长期短期记忆（LSTM）的最先进的 HAR 方法。

PerceptionNet：一种用于延迟传感器融合的深度卷积神经网络

PerceptionNet: A Deep Convolutional Neural Network for Late Sensor  Fusion

Motion sensors such as accelerometers and gyroscopes measure the instant
acceleration and rotation of a device, in three dimensions. Raw data streams
from motion sensors embedded in portable and wearable devices may reveal
private information about users without their awareness. For example, motion
data might disclose the weight or gender of a user, or enable their
re-identification. To address this problem, we propose an on-device
transformation of sensor data to be shared for specific applications, such as
monitoring selected daily activities, without revealing information that
enables user identification. We formulate the anonymization problem using an
information-theoretic approach and propose a new multi-objective loss function
for training deep autoencoders. This loss function helps minimizing
user-identity information as well as data distortion to preserve the
application-specific utility. The training process regulates the encoder to
disregard user-identifiable patterns and tunes the decoder to shape the output
independently of users in the training set. The trained autoencoder can be
deployed on a mobile or wearable device to anonymize sensor data even for users
who are not included in the training dataset. Data from 24 users transformed by
the proposed anonymizing autoencoder lead to a promising trade-off between
utility and privacy, with an accuracy for activity recognition above 92% and an
accuracy for user identification below 7%.

通过信息论方法，设计并使用多目标损失函数训练基于自编码器的移动设备上的传感器数据匿名化方法，实现了保护用户隐私的同时，仍具备监测用户选择日常活动的应用工具。