Incentive mechanism in federated learning
WebIncentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction Pages 947–956 PreviousChapterNextChapter ABSTRACT Current research on … WebWhat is Incentive Mechanisms. 1. A treatment or measure to motivate and encourage people (i.e., to participate in a learning network). Learn more in: Design Guidelines for …
Incentive mechanism in federated learning
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WebDec 4, 2024 · Download Citation On Dec 4, 2024, Jingyuan Liu and others published Incentive Mechanism Design For Federated Learning in Multi-access Edge Computing Find, read and cite all the research you ... WebApr 9, 2024 · Hierarchical Federated Learning (HFL) is a distributed machine learning paradigm tailored for multi-tiered computation architectures, which supports massive access of devices' models simultaneously. To enable efficient HFL, it is crucial to design suitable incentive mechanisms to ensure that devices actively participate in local training. …
WebIn order to effectively solve these problems, we propose FIFL, a fair incentive mechanism for federated learning. FIFL rewards workers fairly to attract reliable and efficient ones while punishing and eliminating the malicious ones based on a dynamic real-time worker assessment mechanism. WebJun 8, 2024 · Federated learning (FL) is an emerging paradigm for machine learning, in which data owners can collaboratively train a model by sharing gradients instead of their raw data. Two fundamental research problems in FL are incentive mechanism and privacy protection. The former focuses on how to incentivize data owners to participate in FL.
WebMar 7, 2024 · Blockchain-based federated learning (BCFL) has recently gained tremendous attention because of its advantages, such as decentralization and privacy protection of raw data. However, there has been few studies focusing on the allocation of resources for the participated devices (i.e., clients) in the BCFL system. Especially, in the BCFL framework …
WebDesign of Two-Level Incentive Mechanisms for Hierarchical Federated Learning Shunfeng Chu, Jun Li, Senior Member, IEEE, Kang Wei, Member, IEEE, Yuwen Qian, Kunlun Wang, Member, IEEE, Feng Shu, Senior Member, IEEE, and Wen Chen, Senior Member, IEEE Abstract—Hierarchical Federated Learning (HFL) is a dis-
Web[10] Zhan Y, Zhang J, Hong Z, et al. A survey of incentive mechanism design for federated learning[J]. IEEE Transactions on Emerging Topics in Computing, 2024. ... Zeng R, Zeng C, … r c sherriff journey\\u0027s endWebJan 20, 2024 · A Learning-Based Incentive Mechanism for Federated Learning Abstract: Internet of Things (IoT) generates large amounts of data at the network edge. Machine … rcs helplineWebMar 3, 2024 · As compared to the current incentive mechanism design in other fields, such as crowdsourcing, cloud computing, smart grid, etc., the incentive mechanism for … r. c. sherriffWebJul 27, 2024 · Incentive Mechanisms in Federated Learning and A Game-Theoretical Approach. Abstract: Federated learning (FL) represents a new machine learning … rc shipyard u-bootWebEnsuring fairness in incentive mechanisms for federated learning (FL) is essential to attracting high-quality clients and building a sustainable FL ecosystem. Most existing … rc shelvesWebAug 9, 2024 · To enable successful interaction among end-devices and aggregation servers for federated learning requires an attractive incentive mechanism. End-devices must be provided with benefits in response to their participation in the federated learning process. rc ship sinksWebIncentive Mechanism Incentive mechanisms have been studied in other areas such as crowdsensing (Gong and Shroff 2024; Yang et al. 2012), but these works have not been directly applied to FL area (Deng et al. 2024). Game theory and auction can be used as approaches to provide incentives for FL (Khan et al. 2024; Zhan et al. 2024). r.c. ships