Results 101 to 110 of about 129,870 (304)
Advanced Intelligent Systems, EarlyView.Artificial intelligence (AI) is reshaping autonomous mobile robot navigation beyond classical pipelines. This review analyzes how AI techniques are integrated into core navigation tasks, including path planning and control, localization and mapping, perception, and context‐aware decision‐making. Learning‐based, probabilistic, and soft‐computing methods
Giovanna Guaragnella +5 more
wiley +1 more source
Challenges and Countermeasures of Federated Learning Data Poisoning Attack Situation Prediction
MathematicsFederated learning is a distributed learning method used to solve data silos and privacy protection in machine learning, aiming to train global models together via multiple clients without sharing data.
Jianping Wu, Jiahe Jin, Chunming Wu
doaj +1 more source
ACM Transactions on Intelligent Systems and Technology, 2019 Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning.
Qiang Yang +3 more
openaire +3 more sources
AI &Innovation, EarlyView.ABSTRACT The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as ...
Amrita Choudhury +2 more
wiley +1 more source
Federated Learning in Data Privacy and Security
Advances in Distributed Computing and Artificial Intelligence JournalFederated learning (FL) has been a rapidly growing topic in recent years. The biggest concern in federated learning is data privacy and cybersecurity. There are many algorithms that federated models have to work on to achieve greater efficiency, security,
Dokuru Trisha Reddy +3 more
doaj +1 more source
Sparse Personalized Federated Learning
IEEE Transactions on Neural Networks and Learning SystemsFederated Learning (FL) is a collaborative machine learning technique to train a global model without obtaining clients' private data. The main challenges in FL are statistical diversity among clients, limited computing capability among clients' equipments, and the excessive communication overhead between the server and clients.
Xiaofeng Liu +5 more
openaire +3 more sources
Confessions of a Poverty Researcher: My Journey Through the Foothills of Scholarship
Australian Journal of Social Issues, EarlyView.ABSTRACT This paper describes the key events, experiences and ideas that influenced the author's career as a poverty researcher. He describes how his early disillusion with economics was replaced by a spark of interest in social issues and how his migration from the UK to Australia in the mid‐1970s provided the impetus to begin what became a lifetime ...
Peter Saunders
wiley +1 more source
Federated Versus Central Machine Learning on Diabetic Foot Ulcer Images: Comparative Simulations
IEEE AccessThis research examines the implementation of the U-Net model within a federated learning framework, focusing on the semantic segmentation of Diabetic Foot Ulcers (DFUs) images.
Mahdi Saeedi +3 more
doaj +1 more source
Adaptive Task Allocation for Asynchronous Federated and Parallelized\n Mobile Edge Learning [PDF]
, 2019 Umair Mohammad, Sameh Sorour
openalex +1 more source
The Politics of Framing the Student Problem: Inquiries Into Australian Civics Education, 2006–2024
Australian Journal of Social Issues, EarlyView.ABSTRACT Recurring debates about civics, the kinds of history that should, and should not, be taught in school, and ‘standards debates’ about the ‘basics’ typically follow on the heels of recurring moral panics about the ‘declining’ state of ‘our’ education system.
Patrick O'Keeffe +2 more
wiley +1 more source