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IMFL: An Incentive Mechanism for Federated Learning With ...
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由 M Li 著作2024被引用 2 次 — We combine game theory to design an FL scheme (incentive mechanism for the FL) based on the incentive mechanism and differential privacy (DP).
IMFL: An Incentive Mechanism for Federated Learning With ...
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由 M Li 著作2024被引用 2 次 — IMFL provides personalized instance-level protection to defend against gradient attacks while ensuring the effectiveness of the global model. In ...
IMFL: An Incentive Mechanism for Federated Learning With ...
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2024年6月26日 — Federated Learning (FL) allows clients to keep local datasets and train collaboratively by uploading model gradients, which achieves the ...
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Abstract: Federated learning (FL) allows clients to keep local data sets and train collaboratively by uploading model gradients, which achieves the goal of ...
IMFL: An Incentive Mechanism for Federated Learning With ...
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IMFL: An Incentive Mechanism for Federated Learning With Personalized Protection. Published:2024-07-01 Issue:13 Volume:11 Page:23862-23877. ISSN:2327-4662.
Junpeng Zhang - Google 学术搜索
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IMFL: An Incentive Mechanism for Federated Learning With Personalized Protection. M Li, Y Tian, J Zhang, Z Zhou, D Zhao, J Ma. IEEE Internet of Things Journal ...
IMFL-AIGC: Incentive Mechanism Design for Federated ...
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由 G Huang 著作2024被引用 4 次 — Federated learning (FL) has emerged as a promising paradigm that enables clients to collaboratively train a shared global model without uploading their local ...
[2406.08526] IMFL-AIGC: Incentive Mechanism Design for ...
arXiv
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由 G Huang 著作2024被引用 4 次 — We first devise a data quality assessment method for data samples generated by AIGC and rigorously analyze the convergence performance of FL model trained.
缺少字詞： Personalized Protection.
Junpeng Zhang's research works | Hebei Normal ...
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Junpeng Zhang's 3 research works with 28 citations, including: IMFL: An Incentive Mechanism for Federated Learning With Personalized Protection.
IMFL-AIGC: Incentive Mechanism Design for Federated ...
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By decoupling model training from the need of direct access of the local data on the devices, FL realizes distributed and privacy-preserving model training.