Results 51 to 60 of about 6,576 (183)
Fairness in Federated Learning: Trends, Challenges, and Opportunities
Advanced Intelligent Systems, Volume 7, Issue 6, June 2025.This survey delves into the intricate issues pertinent to fairness in federated learning , where various biasing factors can skew model performance. By systematically analyzing fairness‐aware strategies, evaluation metrics, and future directions, this work identifies pivotal research gaps in existing approaches and sheds light on both challenges and ...
Noorain Mukhtiar +2 more
wiley +1 more source
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices
, 2015 Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive. The crowds of smart devices offer opportunities to collectively sense and perform computing tasks in an unprecedented scale ...
Belkin, Mikhail +4 more
core +1 more source
IEEE Access, 2021 Emerging vertical applications enabled by connected devices and smart infrastructures have created an ever-increasing demand for high data rates over 5th-Generation (5G) and beyond wireless networks.
M. G. S. Sriyananda +2 more
doaj +1 more source
Software: Practice and Experience, Volume 55, Issue 3, Page 409-447, March 2025.Abstract The progress made in the field of medicine and the consequent increase in the prospect of life have contributed to rise people's interest towards a healthier lifestyle. Fitness activity is becoming a must for those who aspire to live more and better.
Angelo Martella +4 more
wiley +1 more source
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing
, 2018 Sparse Mobile CrowdSensing (MCS) is a novel MCS paradigm where data inference is incorporated into the MCS process for reducing sensing costs while its quality is guaranteed.
Liu, Wenbin +5 more
core +1 more source
IEEE Access, 2022 Mobile Edge Computing (MEC) has been proposed as an efficient solution for Mobile crowdsensing (MCS). It allows the parallel collection and processing of data in real time in response to a requested task.
Hanane Lamaazi +4 more
doaj +1 more source
Bridge monitoring using mobile sensing data with traditional system identification techniques
Computer-Aided Civil and Infrastructure Engineering, Volume 40, Issue 5, Page 579-593, 17 February 2025.Abstract Mobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode ...
Liam Cronin +4 more
wiley +1 more source
Cheating-Resilient Incentive Scheme for Mobile Crowdsensing Systems
, 2017 Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage.
Li, Xin +5 more
core +1 more source
IET Information Security, Volume 2025, Issue 1, 2025.The increasing adoption of artificial intelligence (AI)‐driven unmanned aerial vehicles (UAVs) in military, commercial, and surveillance operations has introduced significant security challenges, including cyber threats, adversarial AI attacks, and communication vulnerabilities. This paper presents a comprehensive review of the key security threats and
Deafallah Alsadie, Jiwei Tian
wiley +1 more source
CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing
, 2017 The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices.
Jin, Haiming, Nahrstedt, Klara, Su, Lu
core +1 more source