Results 11 to 20 of about 1,993,316 (300)
Stochastic Client Selection for Federated Learning With Volatile Clients
IEEE Internet of Things Journal, 2022 Under review by IEEE Internet of Things ...
Tiansheng Huang +4 more
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Client Selection Frameworks Within Federated Machine Learning: The Current Paradigm [PDF]
, 2023 Organisations are increasingly looking for ways to further utilise big data and the benefits that come with this. Previously, this role has been taken by traditional machine learning algorithms.
Jadidi, Zahra +3 more
core +1 more source
Client Selection for Federated Bayesian Learning
IEEE Journal on Selected Areas in Communications, 2023 To appear in IEEE Journal on Selected Areas in Communications Special Issue on Communication-Efficient Distributed Learning over ...
Jiarong Yang +2 more
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Client Selection Method Based on Local Model Quality [PDF]
Jisuanji gongcheng, 2023 Federated learning is a distributed machine learning method that targets environments where data are distributed across multiple clients that collaborate to train models.In an ideal scenario，all clients participate in each round of training，but in ...
WEN Yilin, ZHAO Nailiang, ZENG Yan, HAN Meng, YUE Lupeng, ZHANG Jilin
doaj +1 more source
Empirical Measurement of Client Contribution for Federated Learning With Data Size Diversification
IEEE Access, 2022 Client contribution evaluation is crucial in federated learning(FL) to effectively select influential clients. Contrary to data valuation in centralized settings, client contribution evaluation in FL faces a lack of data accessibility and consequently ...
Sung Kuk Shyn, Donghee Kim, Kwangsu Kim
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Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing
IEEE Access, 2021 Federated Learning (FL) has recently attracted considerable attention in internet of things, due to its capability of enabling mobile clients to collaboratively learn a global prediction model without sharing their privacy-sensitive data to the server ...
Wenyu Zhang +4 more
doaj +1 more source
Transparent and scalable client-side server selection using netlets [PDF]
, 2003 Replication of web content in the Internet has been found to improve service response time, performance and reliability offered by web services. When working with such distributed server systems, the location of servers with respect to client nodes is ...
Martin Collier +5 more
core +1 more source
An Optimization Method for Non-IID Federated Learning Based on Deep Reinforcement Learning
Sensors, 2023 Federated learning (FL) is a distributed machine learning paradigm that enables a large number of clients to collaboratively train models without sharing data.
Xutao Meng +3 more
doaj +1 more source
Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey
Sensors, 2023 This paper explores the potential for communication-efficient federated learning (FL) in modern distributed systems. FL is an emerging distributed machine learning technique that allows for the distributed training of a single machine learning model ...
Muhammad Asad +6 more
doaj +1 more source
Selecting Trustworthy Clients in the Cloud
International Journal on Cloud Computing: Services and Architecture, 2022 With the recent increase demand for cloud services, handling clients’ needs is getting increasingly challenging. Responding to all requesting clients with no exception could lead to security breaches, and since it is the provider’s responsibility to secure not only the offered cloud services but also the data, it is important to ensure clients ...
Imen Bouabdallah, Hakima Mellah
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