Results 11 to 20 of about 17,879 (167)
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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Incentivizing Federated Learning
CoRR, 2022 Federated Learning is an emerging distributed collaborative learning paradigm used by many of applications nowadays. The effectiveness of federated learning relies on clients' collective efforts and their willingness to contribute local data. However, due to privacy concerns and the costs of data collection and model training, clients may not always ...
Shuyu Kong, You Li 0008, Hai Zhou 0001
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CoRR, 2021 Present-day federated learning (FL) systems deployed over edge networks consists of a large number of workers with high degrees of heterogeneity in data and/or computing capabilities, which call for flexible worker participation in terms of timing, effort, data heterogeneity, etc. To satisfy the need for flexible worker participation, we consider a new
Haibo Yang 0001 +3 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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Heterogeneous Federated Learning
CoRR, 2020 Full version [Fed2: Feature-Aligned Federated Learning] accepted in KDD ...
Fuxun Yu +7 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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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
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Hybrid Federated and Centralized Learning [PDF]
2021 29th European Signal Processing Conference (EUSIPCO), 2021 Many of the machine learning (ML) tasks are focused on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) leading to a huge communication overhead. Federated learning (FL) overcomes this issue by allowing the clients to send only the model updates to the PS instead of the whole ...
Ahmet M. Elbir +2 more
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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 +7 more
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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 +3 more
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