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

SplitAMC: Split Learning for Robust Automatic Modulation Classification

Automatic modulation classification (AMC) is a technology that identifies a modulation scheme without prior signal information and plays a vital role in various applications, including cognitive radio and link adaptation. With the development of deep learning (DL), DL-based AMC methods have emerged, while most of them focus on reducing computational complexity in a centralized structure. This centralized learning-based AMC (CentAMC) violates data privacy in the aspect of direct transmission of client-side raw data. Federated learning-based AMC (FedeAMC) can bypass this issue by exchanging model parameters, but causes large resultant latency and client-side computational load. Moreover, both CentAMC and FedeAMC are vulnerable to large-scale noise occured in the wireless channel between the client and the server. To this end, we develop a novel AMC method based on a split learning (SL) framework, coined SplitAMC, that can achieve high accuracy even in poor channel conditions, while guaranteeing data privacy and low latency. In SplitAMC, each client can benefit from data privacy leakage by exchanging smashed data and its gradient instead of raw data, and has robustness to noise with the help of high scale of smashed data. Numerical evaluations validate that SplitAMC outperforms CentAMC and FedeAMC in terms of accuracy for all SNRs as well as latency.Comment: to be presented at IEEE VTC2023-Sprin

Recent advances in label-free imaging and quantification techniques for the study of lipid droplets in cells

Lipid droplets (LDs), once considered mere storage depots for lipids, have gained recognition for their intricate roles in cellular processes, including metabolism, membrane trafficking, and disease states like obesity and cancer. This review explores label-free imaging techniques' applications in LD research. We discuss holotomography and vibrational spectroscopic microscopy, emphasizing their potential for studying LDs without molecular labels, and we highlight the growing integration of artificial intelligence. Clinical applications in disease diagnosis and therapy are also considered

Hexa: Self-Improving for Knowledge-Grounded Dialogue System

A common practice in knowledge-grounded dialogue generation is to explicitly utilize intermediate steps (e.g., web-search, memory retrieval) with modular approaches. However, data for such steps are often inaccessible compared to those of dialogue responses as they are unobservable in an ordinary dialogue. To fill in the absence of these data, we develop a self-improving method to improve the generative performances of intermediate steps without the ground truth data. In particular, we propose a novel bootstrapping scheme with a guided prompt and a modified loss function to enhance the diversity of appropriate self-generated responses. Through experiments on various benchmark datasets, we empirically demonstrate that our method successfully leverages a self-improving mechanism in generating intermediate and final responses and improves the performances on the task of knowledge-grounded dialogue generation

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

Analysis of abnormal muscle activities in patients with loss of cervical lordosis: a cross-sectional study

Background This study aimed to detect the differences in cervical muscle activation patterns in people with versus without cervical lordosis and explore the possible mechanism of cervical pain originating therein. Methods This cross-sectional design included 39 participants without and 18 with normal cervical lordosis. Muscular activation was measured for 5 s in both groups using surface electromyography. Subsequently, the root mean square (RMS) of muscle amplitude was obtained at the bilateral splenius capitis, upper and lower parts of the splenius cervicis, upper and lower parts of the semispinalis cervicis, sternocleidomastoid, upper trapezius, and rhomboid muscles in five cervical positions: 0° (resting), 30° of flexion, 30° of extension, 60° of extension, and upon a 1-kg load on the head in a resting posture. Results The RMS values of the upper trapezius muscle at all postures and the rhomboid muscles at 60° of extension were significantly lower in the loss of lordosis than control group. Comparing the RMS ratio of each posture to the resting position, the ratio of the upper trapezius at flexion was significantly higher and that of the rhomboids at 60° of extension and upon loading was significantly lower in the loss of lordosis than control group. Moreover, the pattern changes in the RMS values according to posture showed a similar shape in these two muscles, and lower in the loss of lordosis than the normal group. Conclusions The loss of normal cervical alignment may correlate with predisposed conditions such as reduced muscle activation of the trapezius and rhomboid muscle, and may also provoke over-firing of the upper trapezius muscle, possibly increasing neck musculoskeletal pain. Trial registration. Clinicaltrials.gov, registration number: NCT03710785.This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI18C1169