Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication overhead should be minimized. With this end, we propose federated distillation (FD), a distributed model training algorithm whose communication payload size is much smaller than a benchmark scheme, federated learning (FL), particularly when the model size is large. Moreover, user-generated data samples are likely to become non-IID across devices, which commonly degrades the performance compared to the case with an IID dataset. To cope with this, we propose federated augmentation (FAug), where each device collectively trains a generative model, and thereby augments its local data towards yielding an IID dataset. Empirical studies demonstrate that FD with FAug yields around 26x less communication overhead while achieving 95-98% test accuracy compared to FL.Comment: presented at the 32nd Conference on Neural Information Processing Systems (NIPS 2018), 2nd Workshop on Machine Learning on the Phone and other Consumer Devices (MLPCD 2), Montr\'eal, Canad

A job analysis of care helpers

The aim of this study was to examine the roles of care helpers through job analysis. To do this, this study used the Developing A Curriculum Method (DACUM) to classify job content and a multi-dimensional study design was applied to identify roles and create a job description by looking into the appropriateness, significance, frequency, and difficulty of job content as identified through workshops and cross-sectional surveys conducted for appropriateness verification. A total of 418 care helpers working in nursing facilities and community senior service facilities across the country were surveyed. The collected data were analyzed using PASW 18.0 software. Six duties and 18 tasks were identified based on the job model. Most tasks were found to be "important task", scoring 4.0 points or above. Physical care duties, elimination care, position changing and movement assistance, feeding assistance, and safety care were identified as high frequency tasks. The most difficult tasks were emergency prevention, early detection, and speedy reporting. A summary of the job of care helpers is providing physical, emotional, housekeeping, and daily activity assistance to elderly patients with problems in independently undertaking daily activities due to physical or mental causes in long-term care facilities or at the client's home. The results of this study suggest a task-focused examination, optimizing the content of the current standard teaching materials authorized by the Ministry of Health and Welfare while supplementing some content which was identified as task elements but not included in the current teaching materials and fully reflecting the actual frequency and difficulty of tasks

Multi-Path and Group-Loss-Based Network for Speech Emotion Recognition in Multi-Domain Datasets

Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IEMOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization

Solar Power Generation Forecasting via Multimodal Feature Fusion (Student Abstract)

Solar power generation has recently been in the spotlight as global warming continues to worsen. However, two significant problems may hinder solar power generation, considering that solar panels are installed outside. The first is soiling, which accumulates on solar panels, and the second is a decrease in sunlight owing to bad weather. In this paper, we will demonstrate that the solar power generation forecasting can increase when considering soiling and sunlight information. We first introduce a dataset containing images of clean and soiled solar panels, sky images, and weather information. For accurate solar power generation forecasting, we propose a new multimodal model that aggregates various features related to weather, soiling, and sunlight. The experimental results demonstrated the high accuracy of our proposed multimodal model

Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning

In this paper, we perform a systematic study about the on-body sensor positioning and data acquisition details for Human Activity Recognition (HAR) systems. We build a testbed that consists of eight body-worn Inertial Measurement Units (IMU) sensors and an Android mobile device for activity data collection. We develop a Long Short-Term Memory (LSTM) network framework to support training of a deep learning model on human activity data, which is acquired in both real-world and controlled environments. From the experiment results, we identify that activity data with sampling rate as low as 10 Hz from four sensors at both sides of wrists, right ankle, and waist is sufficient in recognizing Activities of Daily Living (ADLs) including eating and driving activity. We adopt a two-level ensemble model to combine class-probabilities of multiple sensor modalities, and demonstrate that a classifier-level sensor fusion technique can improve the classification performance. By analyzing the accuracy of each sensor on different types of activity, we elaborate custom weights for multimodal sensor fusion that reflect the characteristic of individual activities

8.214 Embedded Wireless LAN Base-Band Processor for Ubiquitous Computing Systems

Abstract- We propose the ubiquitous computing processor, which contains IEEE802.11 b wireless LAN for a communication channel. IEEE802.llb wireless LAN would play an important role in first generation of ubiquitous computing systems. In this paper, the hardwired 1EEE802.11 b wireless LAN base-band processor, which supports AMBA interface, was implemented and verified with verification environment. As the result of this verification, we demonstrated that implemented wireless base-band processor supports the IEEE8OZ.lIb standard. Also it shows that 1EEESOZ.llb wireless LAN could play a role of the basic download channel in the proposed ubiquitous processor. I