Results 21 to 30 of about 1,993,316 (300)
Clients selection method based on knapsack model in federated learning
物联网学报, 2022 In recent years, to break down data barriers, federated learning (FL) has received extensive attention.In FL, clientscan complete the model training without uploading the raw data, which protects the user’s data privacy.For the issue of clients ...
Jiahui GUO +5 more
doaj +2 more sources
Audit firm, retain or rotation? (client and audit firm perspectives)
Cogent Economics & Finance, 2017 The purpose of this study is providing a framework for understanding the role of audit firm rotation in client expected value. And to explain the principles of client choice by auditor via specified models.
Gholamhossein Mahdavi, Abbas Ali Daryaei
doaj +1 more source
FedSS: Federated Learning with Smart Selection of clients
CoRR, 2022 Federated learning provides the ability to learn over heterogeneous user data in a distributed manner while preserving user privacy. However, its current client selection technique is a source of bias as it discriminates against slow clients. For starters, it selects clients that satisfy certain network and system-specific criteria, thus not selecting ...
Ammar Tahir +2 more
openaire +2 more sources
A Survey on Federated Learning: Fundamentals, Challenges and Client Selection Methods
Al-Rafidain Journal of Computer Sciences and MathematicsFederated learning (FL) represents a transformative shift in machine learning, moving from conventional centralized approaches to a distributed framework that emphasizes data privacy and security.
Alaa Amjad Mala Baker +1 more
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IEEE Open Journal of the Communications Society, 2023 We consider the problem of minimizing the total energy consumption due to the computation and communication tasks of federated learning (FL) under bandwidth and latency constraints. To avoid channel state information (CSI) feedback to the transmitter, we
Mohamed Hany Mahmoud +3 more
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Annual report of the Client Security Fund Committee. CY 2019. [PDF]
, 2020 Annual; Began in 2006?; "Pursuant to Practice Book [section] 2-72(e), the following is a report of claims filed with the Client Security Fund Committee during calendar year ..., and on the Committee's activities during ... in connection with claims filed
Connecticut. Client Security Fund Committee.
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Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
Applied Sciences, 2023 Federated learning (FL) offers a promising solution in edge computing to overcome bandwidth limitations and privacy concerns associated with traditional cloud-based training.
Jian Yang +4 more
doaj +1 more source
A Snapshot of the Frontiers of Client Selection in Federated Learning
CoRR, 2022 17 pages, 3 figures, 1 appendix, accepted to ...
Gergely Dániel Németh +3 more
openaire +4 more sources
AdRo-FL: Secure and Informed Client Selection for Federated Learning Under Adversarial Aggregator
IEEE Open Journal of the Computer SocietyFederated Learning (FL) enables distributed clients, from organizational silos to mobile devices, to collaboratively train a global model. While clients only share model updates, malicious aggregators can infer sensitive information from these updates ...
Md. Kamrul Hossain +4 more
doaj +1 more source
Sensors, 2023 Federated learning (FL) is a technique that allows multiple clients to collaboratively train a global model without sharing their sensitive and bandwidth-hungry data. This paper presents a joint early client termination and local epoch adjustment for FL.
Yi Jie Wong +3 more
doaj +1 more source