D2D computation task offloading for efficient federated learning
Federated learning is a kind of distributed machine learning technique.The factor of communication and computation resource constraints at the edge node is becoming the performance bottleneck.In particular,when different edge node has distinct computation and communication capabilities,the model tra...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2019-12-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2019.00135/ |
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| author | Xiaoran CAI Xiaopeng MO Jie XU |
| author_facet | Xiaoran CAI Xiaopeng MO Jie XU |
| author_sort | Xiaoran CAI |
| collection | DOAJ |
| description | Federated learning is a kind of distributed machine learning technique.The factor of communication and computation resource constraints at the edge node is becoming the performance bottleneck.In particular,when different edge node has distinct computation and communication capabilities,the model training performance may degrade severely,thus necessitating the joint communication and computation optimization.To tackle this challenge,a computational task offloading scheme enabled by device-to-device (D2D) communications was proposed,in which different edge node exchanged data samples via D2D communication links to balance the processing capability and task load,in order to minimize the total time delay for machine learning model training.Simulation results show that compared to the benchmark scheme without such D2D task offloading the training speed and efficiency of federated learning has be improved significantly. |
| format | Article |
| id | doaj-art-89f974e2118442e7a834a99f1c81e290 |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2019-12-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-89f974e2118442e7a834a99f1c81e2902025-01-15T02:52:43ZzhoChina InfoCom Media Group物联网学报2096-37502019-12-013829059645280D2D computation task offloading for efficient federated learningXiaoran CAIXiaopeng MOJie XUFederated learning is a kind of distributed machine learning technique.The factor of communication and computation resource constraints at the edge node is becoming the performance bottleneck.In particular,when different edge node has distinct computation and communication capabilities,the model training performance may degrade severely,thus necessitating the joint communication and computation optimization.To tackle this challenge,a computational task offloading scheme enabled by device-to-device (D2D) communications was proposed,in which different edge node exchanged data samples via D2D communication links to balance the processing capability and task load,in order to minimize the total time delay for machine learning model training.Simulation results show that compared to the benchmark scheme without such D2D task offloading the training speed and efficiency of federated learning has be improved significantly.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2019.00135/federated learningmobile edge computingtask offloadingdevice-to-device communication |
| spellingShingle | Xiaoran CAI Xiaopeng MO Jie XU D2D computation task offloading for efficient federated learning 物联网学报 federated learning mobile edge computing task offloading device-to-device communication |
| title | D2D computation task offloading for efficient federated learning |
| title_full | D2D computation task offloading for efficient federated learning |
| title_fullStr | D2D computation task offloading for efficient federated learning |
| title_full_unstemmed | D2D computation task offloading for efficient federated learning |
| title_short | D2D computation task offloading for efficient federated learning |
| title_sort | d2d computation task offloading for efficient federated learning |
| topic | federated learning mobile edge computing task offloading device-to-device communication |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2019.00135/ |
| work_keys_str_mv | AT xiaorancai d2dcomputationtaskoffloadingforefficientfederatedlearning AT xiaopengmo d2dcomputationtaskoffloadingforefficientfederatedlearning AT jiexu d2dcomputationtaskoffloadingforefficientfederatedlearning |