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Accelerating Fair Federated Learning: Adaptive Federated Adam
IEEE Transactions on Machine Learning in Communications and NetworkingFederated learning is a distributed and privacy-preserving approach to train a statistical model collaboratively from decentralized data held by different parties. However, when the datasets are not independent and identically distributed, models trained
Li Ju +3 more
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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
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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 ...
Rizk, E, Vlaski, S, Sayed, AH
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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
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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 +4 more
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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 +3 more
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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 +4 more
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SAFA : a semi-asynchronous protocol for fast federated learning with low overhead [PDF]
, 2020 Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence.
He, Ligang +5 more
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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 +3 more
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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 +3 more
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