Incentive mechanism in federated learning
WebNov 24, 2024 · The incentive mechanism for federated learning to motivate edge nodes to contribute model training is studied and a deep reinforcement learning-based (DRL) incentive mechanism has been designed to determine the optimal pricing strategy for the parameter server and the optimal training strategies for edge nodes. 192 Highly Influential …
Incentive mechanism in federated learning
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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 … WebJan 1, 2024 · Cross-silo federated learning (FL) is a privacypreserving distributed machine learning where organizations acting as clients cooperatively train a global model without uploading their raw local data.
WebJan 1, 2024 · Request PDF Incentive Mechanism Design for Federated Learning In federated learning, motivating data owners to continue participating in a data federation … WebMar 8, 2024 · Request PDF An Incentive Mechanism for Federated Learning in Wireless Cellular Networks: An Auction Approach Federated Learning (FL) is a distributed learning framework that can deal with the ...
WebJan 1, 2024 · Cross-silo federated learning (FL) is a privacypreserving distributed machine learning where organizations acting as clients cooperatively train a global model without … WebAs the initial variant of federated learning (FL), horizontal federated learning (HFL) applies to the situations where datasets share the same feature space but differ in the sample …
WebEnsuring fairness in incentive mechanisms for federated learning (FL) is essential to attracting high-quality clients and building a sustainable FL ecosystem. Most existing …
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. diane thielfoldtWebMay 1, 2024 · In this work, we propose FGFL, a novel incentive governor for Federated Learning to conduct efficient Federated Learning in the highly heterogeneous and dynamic scenarios. Specifically, FGFL contains two main parts: 1) a fair incentive mechanism and 2) a reliable incentive management system. diane thiele obituaryWebUSENIX The Advanced Computing Systems Association diane thielWebDec 20, 2024 · Federated learning (FL) is a promising distributed machine learning architecture that allows participants to cooperatively train a global model without sharing ... In addition, TBFL leverages a scalable incentive mechanism to enhance its reliability and fairness. We demonstrate the efficacy and attack-resilience of the proposed TBFL through … diane thielenWebDec 1, 2024 · Zeng [28] design the incentive mechanism with a novel multi-dimensional perspective for federated learning. In [36] , [37] , Ding et al. use the contract-theoretic approach to design an optimal incentive mechanism for the parameter server, which considers clients’ multi-dimensional private information, e.g., training overhead and ... citgo by song lyricsWeb[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, Wang X, et al. A comprehensive survey of incentive mechanism for federated learning[J]. arXiv preprint arXiv:2106.15406, 2024. [12] Huang J, Kong L, Chen G, et al ... diane the young and the restlessWebApr 20, 2024 · Federated learning is a new distributed machine learning paradigm that many clients (e.g., mobile devices or organizations) collaboratively train a model under the … diane thiel obituary