Federated learning optimization algorithm based on incentive mechanism
Federated learning optimization algorithm based on incentive mechanism was proposed to address the issues of multiple iterations, long training time and low efficiency in the training process of federated learning.Firstly, the reputation value related to time and model loss was designed.Based on the...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2023-05-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023095/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539203105554432 |
|---|---|
| author | Youliang TIAN Shihong WU Ta LI Lindong WANG Hua ZHOU |
| author_facet | Youliang TIAN Shihong WU Ta LI Lindong WANG Hua ZHOU |
| author_sort | Youliang TIAN |
| collection | DOAJ |
| description | Federated learning optimization algorithm based on incentive mechanism was proposed to address the issues of multiple iterations, long training time and low efficiency in the training process of federated learning.Firstly, the reputation value related to time and model loss was designed.Based on the reputation value, an incentive mechanism was designed to encourage clients with high-quality data to join the training.Secondly, the auction mechanism was designed based on the auction theory.By auctioning local training tasks to the fog node, the client entrusted the high-performance fog node to train local data, so as to improve the efficiency of local training and solve the problem of performance imbalance between clients.Finally, the global gradient aggregation strategy was designed to increase the weight of high-precision local gradient in the global gradient and eliminate malicious clients, so as to reduce the number of model training. |
| format | Article |
| id | doaj-art-7194b093ced644f18dcb1a1c08a42cea |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-05-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-7194b093ced644f18dcb1a1c08a42cea2025-01-14T07:23:56ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-05-014416918059838466Federated learning optimization algorithm based on incentive mechanismYouliang TIANShihong WUTa LILindong WANGHua ZHOUFederated learning optimization algorithm based on incentive mechanism was proposed to address the issues of multiple iterations, long training time and low efficiency in the training process of federated learning.Firstly, the reputation value related to time and model loss was designed.Based on the reputation value, an incentive mechanism was designed to encourage clients with high-quality data to join the training.Secondly, the auction mechanism was designed based on the auction theory.By auctioning local training tasks to the fog node, the client entrusted the high-performance fog node to train local data, so as to improve the efficiency of local training and solve the problem of performance imbalance between clients.Finally, the global gradient aggregation strategy was designed to increase the weight of high-precision local gradient in the global gradient and eliminate malicious clients, so as to reduce the number of model training.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023095/federated learningincentive mechanismreputation valueauction strategyaggregation strategy |
| spellingShingle | Youliang TIAN Shihong WU Ta LI Lindong WANG Hua ZHOU Federated learning optimization algorithm based on incentive mechanism Tongxin xuebao federated learning incentive mechanism reputation value auction strategy aggregation strategy |
| title | Federated learning optimization algorithm based on incentive mechanism |
| title_full | Federated learning optimization algorithm based on incentive mechanism |
| title_fullStr | Federated learning optimization algorithm based on incentive mechanism |
| title_full_unstemmed | Federated learning optimization algorithm based on incentive mechanism |
| title_short | Federated learning optimization algorithm based on incentive mechanism |
| title_sort | federated learning optimization algorithm based on incentive mechanism |
| topic | federated learning incentive mechanism reputation value auction strategy aggregation strategy |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023095/ |
| work_keys_str_mv | AT youliangtian federatedlearningoptimizationalgorithmbasedonincentivemechanism AT shihongwu federatedlearningoptimizationalgorithmbasedonincentivemechanism AT tali federatedlearningoptimizationalgorithmbasedonincentivemechanism AT lindongwang federatedlearningoptimizationalgorithmbasedonincentivemechanism AT huazhou federatedlearningoptimizationalgorithmbasedonincentivemechanism |