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24 papers · ranked by Valyu relevance

01
PPubMed5 Mar 2020
Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care Distributed Learning in Health Care
Fadila Zerka, Samir Barakat, Sean Walsh, Marta Bogowicz + 5 more
Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory…
91relevance
02
AarXiv23 May 2024
Distributed Continual Learning
Long Bao Le, Marcel Hussing, Eric Eaton
This work studies the intersection of continual and federated learning, in which independent agents face unique tasks in their environments and incrementally develop and share knowledge. We introduce a mathematical framework capturing the essential aspects of distributed continual learning, including agent model and…
85relevance
03
BbioRxiv21 Oct 2022
Performance Evaluation of Prediction on Molecular Graphs with Graph Neural Networks
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
85relevance
04
AarXiv1 Dec 2021
Distributed Learning for Time-varying Networks: A Scalable Design
Jian Wang, Yourui Huangfu, Rong Li, Yiqun Ge + 1 more
—The wireless network is undergoing a trend from "connection of things" to "connection of intelligence". With data spread over the communication networks and computing capability enhanced on the devices, distributed learning becomes a hot topic in both industrial and academic communities. Man y frameworks, such as…
84relevance
05
AarXiv26 May 2026
HEAL: Resilient and Self-* Hub-based Learning
Mohamed Amine Legheraba, Stefan Galkiewicz, Maria Gradinariu Potop-Butucaru, Sébastien Tixeuil
Decentralized learning enhances privacy, scalability, and fault tolerance by distributing data and computation across nodes. A popular approach is Federated learning, which relies on a central aggregator, yet faces challenges such as server vulnerabilities, scalability issues, privacy risks and most importantly, the…
83relevance
06
AarXiv2023
A Survey From Distributed Machine Learning to Distributed Deep Learning
Mohammad Dehghani, Zahra Yazdanparast
Artificial intelligence has made remarkable progress in handling complex tasks, thanks to advances in hardware acceleration and machine learning algorithms. However, to acquire more accurate outcomes and solve more complex issues, algorithms should be trained with more data. Processing this huge amount of data could be…
83relevance
07
PPubMed12 Sep 2022
Coding for Large-Scale Distributed Machine Learning
Ming Xiao, Mikael Skoglund, H. Vincent Poor, Onur Günlü + 2 more
'Rafael F. Schaefer' 'Holger Boche'] This article aims to give a comprehensive and rigorous review of the principles and recent development of coding for large-scale distributed machine learning (DML). With increasing data volumes and the pervasive deployment of sensors and computing machines, machine learning has…
83relevance
08
PPubMed25 May 2022
A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data
T. V. Nguyen, M. A. Dakka, S. M. Diakiw, M. D. VerMilyea + 3 more
Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralized for AI training. Data-centric, cross-silo federated learning represents a…
80relevance
09
AarXiv8 Feb 2024
Private Knowledge Sharing in Distributed Learning: A Survey
Yasas Supeksala, Dinh C. Nguyen, Ming Ding, Thilina Ranbaduge + 4 more
'Calson Chua' 'Jun Zhang' 'Jun Li' 'H. Vincent Poor'] YASAS SUPEKSALA, Swinburne University of Technology, Australia DINH C. NGUYEN, Purdue University, USA MING DING, DATA61-CSIRO, Australia THILINA RANBADUGE, DATA61-CSIRO, Australia CALSON CHUA, Swinburne University of Technology, Australia JUN ZHANG, Swinburne…
80relevance
10
CchemRxiv21 Oct 2025
Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
79relevance
11
BbioRxiv29 Jul 2023
A comprehensive experimental comparison between federated and centralized learning
Swier Garst, Julian Dekker, Marcel Reinders
Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be…
79relevance
12
BbioRxiv15 Jun 2025
Predicting continuous outcomes: Some new tests of associative approaches to contingency learning
Julie Y. L. Chow, Hilary J. Don, Ben Colagiuri, Evan J. Livesey
Associative learning models have traditionally simplified contingency learning by relying on binary classification of cues and outcomes, such as administering a medical treatment (or not) and observing whether the patient recovered (or not). While successful in capturing fundamental learning phenomena across human and…
78relevance
13
PPubMed19 Mar 2025
A comprehensive experimental comparison between federated and centralized learning
Swier Garst, Julian Dekker, Marcel Reinders
Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be…
78relevance
14
PPubMed2 Jan 2026
FedPLC: Federated Learning with Dynamic Cluster Adaptation for Concept Drift on Non-IID Data
Qi Zhou, Yantao Yu, Jingxiao Ma, Mohammad S. Obaidat + 4 more
In practical deployments of decentralized federated learning (FL) in Internet of Things (IoT) environments, the non-independent and identically distributed (Non-IID) nature of client-local data limits model performance. Furthermore, concept drift further exacerbates complexity and introduces temporal uncertainty that…
77relevance
15
BbioRxiv6 Feb 2024
UniFed: A unified deep learning framework for segmentation of partially labelled, distributed neuroimaging data
Nicola K Dinsdale, Mark Jenkinson, Ana IL Namburete
It is essential to be able to combine datasets across imaging centres to represent the breadth of biological variability present in clinical populations. This, however, leads to two challenges: first, an increase in non-biological variance due to scanner differences, known as the harmonisation problem, and, second…
76relevance
16
AarXiv2022
Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resources
Ji Liu, Daxiang Dong, Xi Wang, An Qin + 4 more
'Patrick Valduriez' 'Dejing Dou' 'Dianhai Yu'] Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which exceed the capacity of small devices for inference and incurs long training time. In addition…
75relevance
17
BbioRxiv4 Apr 2025
FedNolowe: A Normalized Loss-Based Weighted Aggregation Strategy for Robust Federated Learning in Heterogeneous Environments
Duy-Dong Le, Nguyen Huynh-Tuong, Anh-Khoa Tran, Minh-Son Dao + 1 more
Federated Learning supports collaborative model training across distributed clients while keeping sensitive data decentralized. Still, non-independent and identically distributed data pose challenges like unstable convergence and client drift. We propose Federated Normalized Loss-based Weighted Aggregation (FedNolowe)…
74relevance
18
PPubMed19 Jul 2022
On Distributed Cognition While Designing an AI System for Adapted Learning
Magne V. Aarset, Leiv Kåre Johannessen
When analyzing learning, focus has traditionally been on the teacher, but has in the recent decades slightly moved toward the learner. This is also reflected when supporting systems, both computer-based and more practical equipment, has been introduced. Seeing learning as an integration of both an internal…
74relevance
19
PPubMed7 Sep 2023
Mining multi-center heterogeneous medical data with distributed synthetic learning
Qi Chang, Zhennan Yan, Mu Zhou, Hui Qu + 7 more
Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and ensure the protection of…
73relevance
20
CchemRxiv25 Nov 2025
Federation of toxicological data resources for in silico New Approach Methodologies (NAMs)
Authors not listed
Next Generation Risk Assessment (NGRA) promotes animal-free, exposure-informed, and hypothesis-driven approaches to chemical safety assessment. In silico tools, such as quantitative structure-activity relationship (QSAR) models, are valuable new approach methodologies (NAMs) for use in NGRA. However, the practical…
72relevance
21
BbioRxiv13 Mar 2019
Decentralized Distribution-sampled Classification Models with Application to Brain Imaging
Noah Lewis, Harshvardhan Gazula, Sergey M. Plis, Vince D. Calhoun
In this age of big data, large data stores allow researchers to compose robust models that are accurate and informative. In many cases, the data are stored in separate locations requiring data transfer between local sites, which can cause various practical hurdles, such as privacy concerns or heavy network load. This…
72relevance
22
CchemRxiv6 Feb 2024
Communication efficiency enhanced federated learning derived from quantum reinforcement learning for retrosynthesis
Junjie Hu, Xiangyu Li, Dan-Dan Liu, Shiyi Wang + 3 more
The combination of parametric quantum circuits and density matrix coding can significantly reduce the number of parameters in artificial neural networks. The reduction in the number of model parameters helps to improve the commu- nication efficiency when training deep learning models under federated learning…
70relevance
23
CchemRxiv10 Jul 2025
LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields
Authors not listed
Large Language Models (LLMs) based on transformer architectures excel at internet-scale tasks. However, real-world scientific scenarios—such as synthetic chemistry laboratories and autonomous experimental setups—typically involve incremental data generation in batches as new chemical reactions are conducted, unlike…
60relevance
24
CchemRxiv29 Sep 2025
A Scalable Microservices Platform for Deploying Machine Learning Models in Drug Discovery and Beyond
Authors not listed
Machine learning models are transforming data-driven research across scientific disciplines, yet their deployment as accessible and reliable web services remains a significant challenge. We introduce the NERDD framework, a scalable, maintainable, and secure microservices platform designed to support the sustainable…
57relevance

Results for distributed learning

Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care Distributed Learning in Health Care

Distributed Continual Learning

Performance Evaluation of Prediction on Molecular Graphs with Graph Neural Networks

Distributed Learning for Time-varying Networks: A Scalable Design

HEAL: Resilient and Self-* Hub-based Learning

A Survey From Distributed Machine Learning to Distributed Deep Learning

Coding for Large-Scale Distributed Machine Learning

A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data

Private Knowledge Sharing in Distributed Learning: A Survey

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

A comprehensive experimental comparison between federated and centralized learning

Predicting continuous outcomes: Some new tests of associative approaches to contingency learning

A comprehensive experimental comparison between federated and centralized learning

FedPLC: Federated Learning with Dynamic Cluster Adaptation for Concept Drift on Non-IID Data

UniFed: A unified deep learning framework for segmentation of partially labelled, distributed neuroimaging data

Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resources

FedNolowe: A Normalized Loss-Based Weighted Aggregation Strategy for Robust Federated Learning in Heterogeneous Environments

On Distributed Cognition While Designing an AI System for Adapted Learning

Mining multi-center heterogeneous medical data with distributed synthetic learning

Federation of toxicological data resources for in silico New Approach Methodologies (NAMs)

Decentralized Distribution-sampled Classification Models with Application to Brain Imaging

Communication efficiency enhanced federated learning derived from quantum reinforcement learning for retrosynthesis

LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields

A Scalable Microservices Platform for Deploying Machine Learning Models in Drug Discovery and Beyond

Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care Distributed Learning in Health Care

Distributed Continual Learning

Performance Evaluation of Prediction on Molecular Graphs with Graph Neural Networks

Distributed Learning for Time-varying Networks: A Scalable Design

HEAL: Resilient and Self-* Hub-based Learning

A Survey From Distributed Machine Learning to Distributed Deep Learning

Coding for Large-Scale Distributed Machine Learning

A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data

Private Knowledge Sharing in Distributed Learning: A Survey

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

A comprehensive experimental comparison between federated and centralized learning

Predicting continuous outcomes: Some new tests of associative approaches to contingency learning

A comprehensive experimental comparison between federated and centralized learning

FedPLC: Federated Learning with Dynamic Cluster Adaptation for Concept Drift on Non-IID Data

UniFed: A unified deep learning framework for segmentation of partially labelled, distributed neuroimaging data

Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resources

FedNolowe: A Normalized Loss-Based Weighted Aggregation Strategy for Robust Federated Learning in Heterogeneous Environments

On Distributed Cognition While Designing an AI System for Adapted Learning

Mining multi-center heterogeneous medical data with distributed synthetic learning

Federation of toxicological data resources for in silico New Approach Methodologies (NAMs)

Decentralized Distribution-sampled Classification Models with Application to Brain Imaging

Communication efficiency enhanced federated learning derived from quantum reinforcement learning for retrosynthesis

LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields

A Scalable Microservices Platform for Deploying Machine Learning Models in Drug Discovery and Beyond