Byzantine-robust federated learning over Non-IID data
The malicious attacks of Byzantine nodes in federated learning was studied over the non-independent and identically distributed dataset , and a privacy protection robust gradient aggregation algorithm was proposed.A reference gradient was designed to identify “poor quality” shared gradients in model...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023115/ |
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| _version_ | 1841540021088157696 |
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| author | Xindi MA Qinghua LI Qi JIANG Zhuo MA Sheng GAO Youliang TIAN Jianfeng MA |
| author_facet | Xindi MA Qinghua LI Qi JIANG Zhuo MA Sheng GAO Youliang TIAN Jianfeng MA |
| author_sort | Xindi MA |
| collection | DOAJ |
| description | The malicious attacks of Byzantine nodes in federated learning was studied over the non-independent and identically distributed dataset , and a privacy protection robust gradient aggregation algorithm was proposed.A reference gradient was designed to identify “poor quality” shared gradients in model training, and the influence of heterogeneity data on Byzantine node recognition was reduced by reputation evaluation.Meanwhile, the combination of homomorphic encryption and random noise obfuscation technology was introduced to protect user privacy in the process of model training and Byzantine node recognition.Finally, through the evaluation over the real-world datasets, the simulation results show that the proposed algorithm can accurately and efficiently identify Byzantine attack nodes while protecting user privacy and has good convergence and robustness. |
| format | Article |
| id | doaj-art-204429325c1b4e5d8eb08fe2c9d0919f |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-204429325c1b4e5d8eb08fe2c9d0919f2025-01-14T06:23:00ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-06-014413815359386536Byzantine-robust federated learning over Non-IID dataXindi MAQinghua LIQi JIANGZhuo MASheng GAOYouliang TIANJianfeng MAThe malicious attacks of Byzantine nodes in federated learning was studied over the non-independent and identically distributed dataset , and a privacy protection robust gradient aggregation algorithm was proposed.A reference gradient was designed to identify “poor quality” shared gradients in model training, and the influence of heterogeneity data on Byzantine node recognition was reduced by reputation evaluation.Meanwhile, the combination of homomorphic encryption and random noise obfuscation technology was introduced to protect user privacy in the process of model training and Byzantine node recognition.Finally, through the evaluation over the real-world datasets, the simulation results show that the proposed algorithm can accurately and efficiently identify Byzantine attack nodes while protecting user privacy and has good convergence and robustness.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023115/federated learningByzantine attackNon-IIDprivacy protectionhomomorphic encryption |
| spellingShingle | Xindi MA Qinghua LI Qi JIANG Zhuo MA Sheng GAO Youliang TIAN Jianfeng MA Byzantine-robust federated learning over Non-IID data Tongxin xuebao federated learning Byzantine attack Non-IID privacy protection homomorphic encryption |
| title | Byzantine-robust federated learning over Non-IID data |
| title_full | Byzantine-robust federated learning over Non-IID data |
| title_fullStr | Byzantine-robust federated learning over Non-IID data |
| title_full_unstemmed | Byzantine-robust federated learning over Non-IID data |
| title_short | Byzantine-robust federated learning over Non-IID data |
| title_sort | byzantine robust federated learning over non iid data |
| topic | federated learning Byzantine attack Non-IID privacy protection homomorphic encryption |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023115/ |
| work_keys_str_mv | AT xindima byzantinerobustfederatedlearningovernoniiddata AT qinghuali byzantinerobustfederatedlearningovernoniiddata AT qijiang byzantinerobustfederatedlearningovernoniiddata AT zhuoma byzantinerobustfederatedlearningovernoniiddata AT shenggao byzantinerobustfederatedlearningovernoniiddata AT youliangtian byzantinerobustfederatedlearningovernoniiddata AT jianfengma byzantinerobustfederatedlearningovernoniiddata |