51 research outputs found
Client Selection in Federated Learning: Principles, Challenges, and Opportunities
As a privacy-preserving paradigm for training Machine Learning (ML) models,
Federated Learning (FL) has received tremendous attention from both industry
and academia. In a typical FL scenario, clients exhibit significant
heterogeneity in terms of data distribution and hardware configurations. Thus,
randomly sampling clients in each training round may not fully exploit the
local updates from heterogeneous clients, resulting in lower model accuracy,
slower convergence rate, degraded fairness, etc. To tackle the FL client
heterogeneity problem, various client selection algorithms have been developed,
showing promising performance improvement. In this paper, we systematically
present recent advances in the emerging field of FL client selection and its
challenges and research opportunities. We hope to facilitate practitioners in
choosing the most suitable client selection mechanisms for their applications,
as well as inspire researchers and newcomers to better understand this exciting
research topic
Federated Learning Hyper-Parameter Tuning from a System Perspective
Federated learning (FL) is a distributed model training paradigm that
preserves clients' data privacy. It has gained tremendous attention from both
academia and industry. FL hyper-parameters (e.g., the number of selected
clients and the number of training passes) significantly affect the training
overhead in terms of computation time, transmission time, computation load, and
transmission load. However, the current practice of manually selecting FL
hyper-parameters imposes a heavy burden on FL practitioners because
applications have different training preferences. In this paper, we propose
FedTune, an automatic FL hyper-parameter tuning algorithm tailored to
applications' diverse system requirements in FL training. FedTune iteratively
adjusts FL hyper-parameters during FL training and can be easily integrated
into existing FL systems. Through extensive evaluations of FedTune for diverse
applications and FL aggregation algorithms, we show that FedTune is lightweight
and effective, achieving 8.48%-26.75% system overhead reduction compared to
using fixed FL hyper-parameters. This paper assists FL practitioners in
designing high-performance FL training solutions. The source code of FedTune is
available at https://github.com/DataSysTech/FedTune.Comment: arXiv admin note: substantial text overlap with arXiv:2110.0306
Federated Learning Hyper-Parameter Tuning for Edge Computing
Edge computing is widely recognized as a crucial technology for the upcoming generation of communication networks and has garnered significant interest from both industry and academia. Compared to other offloading models like cloud computing, it provides faster data processing capabilities, enhanced security measures, and lower costs by leveraging the proximity of the edge servers to the end devices. This helps mitigate the privacy concerns associated with data transfer in edge computing, by reducing the distance between the data source and the server. Raw data in typical edge computing scenarios still need to be sent to the edge server, leading to data leakage and privacy breaches. Federated Learning (FL) is a distributed model training paradigm that preserves end devices’ data privacy. Therefore, it is crucial to incorporate FL into edge computing to protect data privacy. However, the high training overhead of FL makes it impractical for edge computing. In this study, we propose to facilitate the integration of FL and edge computing by optimizing FL hyper-parameters, which can significantly reduce FL’s training overhead and make it more affordable for edge computing
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence
With the rapid development of the internet of things (IoT) and artificial
intelligence (AI) technologies, human activity recognition (HAR) has been
applied in a variety of domains such as security and surveillance, human-robot
interaction, and entertainment. Even though a number of surveys and review
papers have been published, there is a lack of HAR overview papers focusing on
healthcare applications that use wearable sensors. Therefore, we fill in the
gap by presenting this overview paper. In particular, we present our projects
to illustrate the system design of HAR applications for healthcare. Our
projects include early mobility identification of human activities for
intensive care unit (ICU) patients and gait analysis of Duchenne muscular
dystrophy (DMD) patients. We cover essential components of designing HAR
systems including sensor factors (e.g., type, number, and placement location),
AI model selection (e.g., classical machine learning models versus deep
learning models), and feature engineering. In addition, we highlight the
challenges of such healthcare-oriented HAR systems and propose several research
opportunities for both the medical and the computer science community
Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches
Differences in gait patterns of children with Duchenne muscular dystrophy
(DMD) and typically-developing (TD) peers are visible to the eye, but
quantifications of those differences outside of the gait laboratory have been
elusive. In this work, we measured vertical, mediolateral, and anteroposterior
acceleration using a waist-worn iPhone accelerometer during ambulation across a
typical range of velocities. Fifteen TD and fifteen DMD children from 3-16
years of age underwent eight walking/running activities, including five 25
meters walk/run speed-calibration tests at a slow walk to running speeds (SC-L1
to SC-L5), a 6-minute walk test (6MWT), a 100 meters fast-walk/jog/run
(100MRW), and a free walk (FW). For clinical anchoring purposes, participants
completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial
gait clinical features (CFs) and applied multiple machine learning (ML)
approaches to differentiate between DMD and TD children using extracted
temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed
reduced step length and a greater mediolateral component of total power (TP)
consistent with shorter strides and Trendelenberg-like gait commonly observed
in DMD. ML approaches using temporospatial gait CFs and raw data varied in
effectiveness at differentiating between DMD and TD controls at different
speeds, with an accuracy of up to 100%. We demonstrate that by using ML with
accelerometer data from a consumer-grade smartphone, we can capture
DMD-associated gait characteristics in toddlers to teens
Molecular Engineered Hole-Extraction Materials to Enable Dopant-Free, Efficient p-i-n Perovskite Solar Cells
Two hole-extraction materials (HEMs), TPP-OMeTAD and TPP-SMeTAD, have been developed to facilitate the fabrication of efficient p-i-n perovskite solar cells (PVSCs). By replacing the oxygen atom on HEM with sulfur (from TPP-OMeTAD to TPP-SMeTAD), it effectively lowers the highest occupied molecular orbital of the molecule and provides stronger Pb-S interaction with perovskites, leading to efficient charge extraction and surface traps passivation. The TPP-SMeTAD-based PVSCs exhibit both improved photovoltaic performance and reduced hysteresis in p-i-n PVSCs over those based on TPP-OMeTAD. This work not only provides new insights on creating perovskite-HEM heterojunction but also helps in designing new HEM to enable efficient organic–inorganic hybrid PVSCs
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