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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.
Stefan Jonas, Angela Meyer
exaly   +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

Vulnerabilities in Federated Learning [PDF]

IEEE Access, 2021
With more regulations tackling the protection of users’ privacy-sensitive data in recent years, access to such data has become increasingly restricted. A new decentralized training paradigm, known as Federated Learning (FL), enables multiple clients located at different geographical locations to learn a machine learning model collaboratively ...
Nader Bouacida, Prasant Mohapatra
openaire   +2 more sources

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

Learning to Backdoor Federated Learning

CoRR, 2023
In a federated learning (FL) system, malicious participants can easily embed backdoors into the aggregated model while maintaining the model's performance on the main task. To this end, various defenses, including training stage aggregation-based defenses and post-training mitigation defenses, have been proposed recently.
Henger Li, Chen Wu, Sencun Zhu, Zizhan Zheng   +3 more
openaire   +2 more sources

A Survey of Federated Evaluation in Federated Learning

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server. However, model evaluation becomes a challenging problem in federated learning (FL), which is called federated evaluation in this work.
Behnaz Soltani, Yipeng Zhou, Venus Haghighi, John C. S. Lui   +3 more
openaire   +2 more sources

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

Preconditioned Federated Learning

CoRR, 2023
Federated Learning (FL) is a distributed machine learning approach that enables model training in communication efficient and privacy-preserving manner. The standard optimization method in FL is Federated Averaging (FedAvg), which performs multiple local SGD steps between communication rounds.
Zeyi Tao, Jindi Wu, Qun Li 0001
openaire   +2 more sources

Dynamic Federated Learning [PDF]

2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2020
Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyses assume static optimization problems and offer no guarantees in the presence of drifts in the ...
Elsa Rizk, Stefan Vlaski, Ali H. Sayed
openaire   +2 more sources

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