Results 61 to 70 of about 72,357 (237)

Federated Learning in Computer Vision

IEEE Access, 2023
Federated Learning (FL) has recently emerged as a novel machine learning paradigm allowing to preserve privacy and to account for the distributed nature of the learning process in many real-world settings.
Donald Shenaj, Giulia Rizzoli, Pietro Zanuttigh   +2 more
doaj   +1 more source

VeryFL: A Verify Federated Learning Framework Embedded with Blockchain

, 2023
Blockchain-empowered federated learning (FL) has provoked extensive research recently. Various blockchain-based federated learning algorithm, architecture and mechanism have been designed to solve issues like single point failure and data falsification ...
Chen, Chuan, Lai, Yanyi, Li, Yihao, Zheng, Zibin   +3 more
core  

TUNE-FL: Adaptive Semi-Synchronous Semi-Decentralized Federated Learning

2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC)
Today, Federated Learning (FL) stands out as the solution to addressing the challenges of distributed computing and empowering a wide range of edge devices with artificial intelligence capabilities. One variant of FL called semi-decentralized FL (SDFL) enables multiple server units to coordinate the learning task instead of relying on only one central
Jmal, Houssem, Piamrat, Kandaraj, Aouedi, Ons   +2 more
openaire   +2 more sources

A Holistic Wellness Prescription for Parkinson's Disease: Evidence‐Based Perspectives and Unmet Needs

Movement Disorders Clinical Practice, EarlyView.
Abstract Background In modern medicine the concept of wellness is often accompanied by various misconceptions arising from several factors, including a lack of clear definitions, the commercialization of wellness, and prevailing biases and stereotypes.
Indu Subramanian, Lucia Ricciardi, Anette Schrag, Silke Appel‐Cresswell, Sushma Kola, Josefa M. Domingos, Barbara A. Pickut, Nabila Dahodwala, Abdoulaye Bocoum, Cristian Falup‐Pecurariu, Gila Bronner, Gregory M. Pontone, Laurie K. Mischley, Nélida S. Garretto, Nicola Modugno, Rachel Dolhun, Tissa Wijeratne, Victoria Tull, Bradley McDaniels, K. Ray Chaudhuri, the International Parkinson, Movement Disorder Society Task Force on Wellness, Indu Subramanian, Lucia Ricciardi, Anette Schrag, Silke Appel‐Cresswell, Sushma Kola, Josefa M. Domingos, Barbara A. Pickut, Nabila Dahodwala, Abdoulaye Bocoum, Cristian Falup‐Pecurariu, Gila Bronner, Gregory M. Pontone, Laurie K. Mischley, Nélida S. Garretto, Nicola Modugno, Rachel Dolhun, Tissa Wijeratne, Victoria Tull, K. Ray Chaudhuri   +40 more
wiley   +1 more source

A Study of Federated Deep Learning for Building Indoor Climate Forecasting

IEEE Access
This study investigates federated deep learning for multi-horizon indoor climate forecasting in historic buildings. Unlike traditional centralized or isolated local learning approaches, this work explores federated learning (FL) as a solution that ...
Zhongjun Ni, Magnus Karlsson, Shaofang Gong   +2 more
doaj   +1 more source

FL-IDS: Federated Learning-Based Intrusion Detection System Using Edge Devices for Transportation IoT

IEEE Access
A federated learning-based intrusion detection system (FL-IDS) is introduced to enhance the security of vehicular networks in the context of IoT edge device implementations.
Mansi Bhavsar, Y. Bekele, K. Roy, John Kelly, Daniel B. Limbrick   +4 more
semanticscholar   +1 more source

MarS-FL: Enabling Competitors to Collaborate in Federated Learning

IEEE Transactions on Big Data
Federated learning (FL) is rapidly gaining popularity and enables multiple data owners ({\em a.k.a.} FL participants) to collaboratively train machine learning models in a privacy-preserving way. A key unaddressed scenario is that these FL participants are in a competitive market, where market shares represent their competitiveness.
Xiaohu Wu, Han Yu
openaire   +3 more sources

Six artificial intelligence innovation strategies applied to autism spectrum disorder research: A narrative review

Pediatric Investigation, EarlyView.
Six artificial intelligence strategies advance autism research from tool optimization to paradigm shift: causal modeling, spatiotemporal networks, multimodal integration, digital twins, social cognition mapping, collaborative learning, and context‐aware interventions for precision care.
Ting Zhang, Zhenkun Cao, Wei Li, Zhihai Lv   +3 more
wiley   +1 more source

Examining the Impact of Unified Management on Board Roles in Federated Governance Systems: A Study of Golf in Australia

Systems Research and Behavioral Science, EarlyView.
ABSTRACT The exploration of ways to address the complexity of relationships, power dynamics and multiple perspectives within federated governance systems in sport has been an ongoing theme within sport governance scholarly and practice communities for several decades.
Ian O'Boyle, Lesley Ferkins, Joshua McLeod, David Shilbury, Leila Heckel   +4 more
wiley   +1 more source

FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model [PDF]

Knowledge Discovery and Data Mining
Large language models (LLMs) show amazing performance on many domain-specific tasks after fine-tuning with some appropriate data. However, many domain-specific data are privately distributed across multiple owners.
Feijie Wu, Zitao Li, Yaliang Li, Bolin Ding, Jing Gao   +4 more
semanticscholar   +1 more source