Results 41 to 50 of about 72,357 (237)

Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks

Sensors
LoRa networks, widely adopted for low-power, long-range communication in IoT applications, face critical security concerns as radio-frequency transmissions are increasingly vulnerable to tampering.
Nurettin Selcuk Senol, Mohamed Baza, Amar Rasheed, Maazen Alsabaan   +3 more
doaj   +1 more source

PersA-FL : personalized asynchronous federated learning

Optimization Methods and Software
We study the personalized federated learning problem under asynchronous updates. In this problem, each client seeks to obtain a personalized model that simultaneously outperforms local and global models. We consider two optimization-based frameworks for personalization: (i) Model-Agnostic Meta-Learning (MAML) and (ii) Moreau Envelope (ME).
Mohammad Taha Toghani, Soomin Lee, César A. Uribe   +2 more
openaire   +2 more sources

A Semi-Federated Active Learning Framework for Unlabeled Online Network Data

Mathematics, 2023
Federated Learning (FL) is a newly emerged federated optimization technique for distributed data in a federated network. The participants in FL that train the model locally are classified into client nodes.
Yuwen Zhou, Yuhan Hu, Jing Sun, Rui He, Wenjie Kang   +4 more
doaj   +1 more source

A Bibliometric Analysis on Federated Learning

Journal of Advanced Research in Natural and Applied Sciences
With the rapid advancement of technology and growing concerns about data privacy, federated learning (FL) has attracted considerable attention from the scientific community.
Ömer Algorabi, Mesut Ulu, Yusuf Sait Türkan, Ersin Namlı   +3 more
doaj   +1 more source

Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

, 2018
We envision a mobile edge computing (MEC) framework for machine learning (ML) technologies, which leverages distributed client data and computation resources for training high-performance ML models while preserving client privacy. Toward this future goal,
Nishio, Takayuki, Yonetani, Ryo
core   +1 more source

FedDK: Improving Cyclic Knowledge Distillation for Personalized Healthcare Federated Learning

IEEE Access, 2023
For most healthcare organizations, a significant challenge today is predicting diseases with incomplete data information, often resulting in isolation.
Yikai Xu, Hongbo Fan
doaj   +1 more source

Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

, 2023
On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication overhead should be minimized.
Bennis, Mehdi, Jeong, Eunjeong, Kim, Hyesung, Kim, Seong-Lyun, Oh, Seungeun, Park, Jihong   +5 more
core  

Decentralized Machine Learning Training: A Survey on Synchronization, Consolidation, and Topologies

IEEE Access, 2023
Federated Learning (FL) has emerged as a promising methodology for collaboratively training machine learning models on decentralized devices. Notwithstanding, the effective synchronization and consolidation of model updates originating from diverse ...
Qazi Waqas Khan, Anam Nawaz Khan, Atif Rizwan, Rashid Ahmad, Salabat Khan, Do-Hyeun Kim   +5 more
doaj   +1 more source

LiD-FL: Towards List-Decodable Federated Learning

Proceedings of the AAAI Conference on Artificial Intelligence
Federated learning is often used in environments with many unverified participants. Therefore, federated learning under adversarial attacks receives significant attention. This paper proposes an algorithmic framework for list-decodable federated learning, where a central server maintains a list of models, with at least one guaranteed to perform well ...
Liu, Hong, Shan, Liren, Bao, Han, You, Ronghui, Yi, Yuhao, Lv, Jiancheng   +5 more
openaire   +2 more sources

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source