Communication Efficient Distributed Newton Method over Unreliable Networks
Distributed optimization in resource constrained devices demands both communication
efficiency and fast convergence rates. Newton-type methods are getting preferable due to …
efficiency and fast convergence rates. Newton-type methods are getting preferable due to …
Resource-constrained multisource instance-based transfer learning
M Askarizadeh, A Morsali… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In today's machine learning (ML), the need for vast amounts of training data has become a
significant challenge. Transfer learning (TL) offers a promising solution by leveraging …
significant challenge. Transfer learning (TL) offers a promising solution by leveraging …
Private Knowledge Sharing in Distributed Learning: A Survey
Y Supeksala, DC Nguyen, M Ding… - arXiv preprint arXiv …, 2024 - arxiv.org
The rise of Artificial Intelligence (AI) has revolutionized numerous industries and transformed
the way society operates. Its widespread use has led to the distribution of AI and its …
the way society operates. Its widespread use has led to the distribution of AI and its …
Leveraging Network Data Analytics Function and Machine Learning for Data Collection, Resource Optimization, Security and Privacy in 6G Networks
The full deployment of sixth-generation (6G) networks is inextricably connected with a
holistic network redesign able to deal with various emerging challenges, such as integration …
holistic network redesign able to deal with various emerging challenges, such as integration …
Demultiplexing OAM beams via Fourier optical convolutional neural network
Here we present an innovative free-space optical (FSO) communication system which is
capable of training database in real-time and demultiplex multiplexed spatial structured …
capable of training database in real-time and demultiplex multiplexed spatial structured …
Distributed Machine Learning and Native AI Enablers for End-to-End Resources Management in 6G
OA Karachalios, A Zafeiropoulos, K Kontovasilis… - Electronics, 2023 - mdpi.com
6G targets a broad and ambitious range of networking scenarios with stringent and diverse
requirements. Such challenging demands require a multitude of computational and …
requirements. Such challenging demands require a multitude of computational and …
Multi-Agent Multi-Armed Bandit Learning for Grant-Free Access in Ultra-Dense IoT Networks
Meeting the diverse quality-of-service (QoS) requirements in ultra-dense Internet of Things
(IoT) networks operating under varying network loads is challenging. Moreover, latency …
(IoT) networks operating under varying network loads is challenging. Moreover, latency …
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights
Artificial Intelligence (AI) is expected to play an instrumental role in the next generation of
wireless systems, such as sixth-generation (6G) mobile network. However, massive data …
wireless systems, such as sixth-generation (6G) mobile network. However, massive data …
Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach
MFU Abrar, N Michelusi - 2023 57th Asilomar Conference on …, 2023 - ieeexplore.ieee.org
Over-the-air (OTA) computation has recently emerged as a communication-efficient
Federated Learning (FL) paradigm to train machine learning models over wireless networks …
Federated Learning (FL) paradigm to train machine learning models over wireless networks …
Heterogeneous Ensemble Federated Learning with GAN-based Privacy Preservation
M Chen, H Liu, H Chi, P Xiong - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Multi-party collaborative learning has become a paradigm for large-scale knowledge
discovery in the era of big data. As a typical form of collaborative learning, federated …
discovery in the era of big data. As a typical form of collaborative learning, federated …