FIFL: A Fair Incentive Mechanism for Federated Learning-Reference-Cited by-同舟云学术

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FIFL: A Fair Incentive Mechanism for Federated Learning

Published:2021-08-09 Issue: Volume: Page:
ISSN:
Container-title:50th International Conference on Parallel Processing
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Author:

Gao Liang¹,Li Li²,Chen Yingwen³,Zheng Wenli⁴,Xu ChengZhong⁵,Xu Ming³

Affiliation:

1. National University of Defense Technology

2. ShenZhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

3. National University of Defense Technology, China

4. Emerging Parallel Computing Center， Shanghai Jiao Tong University

5. University of Macau, Macau

Funder

Research on Privacy-Preserving Issues Within Smart City Truth Discovery Process

NUDT Research Grants

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3472456.3472469

Reference34 articles.

1. Trust Assessment in Vehicular Social Network Based on Three-Valued Subjective Logic

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1. Fair swarm learning: Improving incentives for collaboration by a fair reward mechanism;Knowledge-Based Systems;2024-11

2. Research on Data Quality Governance for Federated Cooperation Scenarios;Electronics;2024-09-11

3. Chiron: A Robustness-Aware Incentive Scheme for Edge Learning via Hierarchical Reinforcement Learning;IEEE Transactions on Mobile Computing;2024-08

4. Hire When You Need to: Gradual Participant Recruitment for Auction-Based Federated Learning;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

5. Flagger: Cooperative Acceleration for Large-Scale Cross-Silo Federated Learning Aggregation;2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA);2024-06-29

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