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A review of federated learning in renewable energy applications: Potential, challenges, and future directions

Energy and AI
Federated learning has recently emerged as a privacy-preserving distributed machine learning approach. Federated learning enables collaborative training of multiple clients and entire fleets without sharing the involved training datasets.
Albin Grataloup, Stefan Jonas, Angela Meyer   +2 more
doaj   +3 more sources

Survey of Graph Neural Network [PDF]

Jisuanji gongcheng, 2021
With the continuous development of the computer and Internet technologies,graph neural network has become an important research area in artificial intelligence and big data.Graph neural network can effectively transmit and aggregate information between ...
WANG Jianzong, KONG Lingwei, HUANG Zhangcheng, XIAO Jing
doaj   +1 more source

Efficient Federated Learning Scheme Based on Game Theory Optimization [PDF]

Jisuanji gongcheng, 2022
With the continuous development of network information technology and Internet technology, data privacy and security issues need to be addressed urgently.Federated learning has emerged as a new distributed privacy protection machine learning framework ...
ZHOU Quanxing, LI Qiuxian, DING Hongfa, FAN Meimei
doaj   +1 more source

Fairness-Based Multi-AP Coordination Using Federated Learning in Wi-Fi 7

Sensors, 2022
Federated learning is a type of distributed machine learning in which models learn by using large-scale decentralized data between servers and devices. In a short-range wireless communication environment, it can be difficult to apply federated learning ...
Gimoon Woo, Hyungbin Kim, Seunghyun Park, Cheolwoo You, Hyunhee Park   +4 more
doaj   +1 more source

LAFD: Local-Differentially Private and Asynchronous Federated Learning With Direct Feedback Alignment

IEEE Access, 2023
Federated learning is a promising approach for training machine learning models using distributed data from multiple mobile devices. However, privacy concerns arise when sensitive data are used for training.
Kijung Jung, Incheol Baek, Soohyung Kim, Yon Dohn Chung   +3 more
doaj   +1 more source

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

IEEE Access, 2021
In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based
Mohamed Amine Ferrag, Othmane Friha, Leandros Maglaras, Helge Janicke, Lei Shu   +4 more
doaj   +1 more source

DWFed: A statistical- heterogeneity-based dynamic weighted model aggregation algorithm for federated learning

Frontiers in Neurorobotics, 2022
Federated Learning is a distributed machine learning framework that aims to train a global shared model while keeping their data locally, and previous researches have empirically proven the ideal performance of federated learning methods. However, recent
Aiguo Chen, Yang Fu, Lingfu Wang, Guiduo Duan   +3 more
doaj   +1 more source

The Cost of Training Machine Learning Models Over Distributed Data Sources

IEEE Open Journal of the Communications Society, 2023
Federated learning is one of the most appealing alternatives to the standard centralized learning paradigm, allowing a heterogeneous set of devices to train a machine learning model without sharing their raw data. However, it requires a central server to
Elia Guerra, Francesc Wilhelmi, Marco Miozzo, Paolo Dini   +3 more
doaj   +1 more source

Review on application progress of federated learning model and security hazard protection

Digital Communications and Networks, 2023
Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy.
Aimin Yang, Zezhong Ma, Chunying Zhang, Yang Han, Zhibin Hu, Wei Zhang, Xiangdong Huang, Yafeng Wu   +7 more
doaj   +1 more source

A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security

Applied Sciences, 2023
The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues ...
Yihang Luo, Bei Gong, Haotian Zhu, Chong Guo   +3 more
doaj   +1 more source