Personalized Federated Learning Scheme for Autonomous Driving Based on Correlated Differential Privacy
In the era of big data, advanced data processing devices and smart sensors greatly benefit us in many areas. As for each individual user, data sharing can be an essential part of the process of data collection and transmission. However, the issue of constant attacks on data privacy arouses huge conc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/178 |
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| author | Yuan Tian Yanfeng Shi Yue Zhang Qikun Tian |
| author_facet | Yuan Tian Yanfeng Shi Yue Zhang Qikun Tian |
| author_sort | Yuan Tian |
| collection | DOAJ |
| description | In the era of big data, advanced data processing devices and smart sensors greatly benefit us in many areas. As for each individual user, data sharing can be an essential part of the process of data collection and transmission. However, the issue of constant attacks on data privacy arouses huge concerns among the public. This work proposes a personalized federated learning method associated with correlated differential privacy for autonomous driving. First, instead of transmitting raw data to the server following collection, a device that employs federated learning can perform calculations to obtain the training model at each node. Second, we specifically perform a correlated classification analysis to encrypt data that share high relevance, which can minimize the system cost. Then, correlated differential privacy is utilized to achieve the preservation of data privacy before sharing. In contrast to the traditional differential privacy, the proposed solution guarantees enhanced privacy to meet the demands of customization. The experimental results show that our scheme is more refined in terms of user heterogeneity and the utility of data than others without violating privacy. |
| format | Article |
| id | doaj-art-41f93553331e4beea12ec3a0eae43e86 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-41f93553331e4beea12ec3a0eae43e862025-01-10T13:21:08ZengMDPI AGSensors1424-82202024-12-0125117810.3390/s25010178Personalized Federated Learning Scheme for Autonomous Driving Based on Correlated Differential PrivacyYuan Tian0Yanfeng Shi1Yue Zhang2Qikun Tian3School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaElectrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, 10044 Stockholm, SwedenIn the era of big data, advanced data processing devices and smart sensors greatly benefit us in many areas. As for each individual user, data sharing can be an essential part of the process of data collection and transmission. However, the issue of constant attacks on data privacy arouses huge concerns among the public. This work proposes a personalized federated learning method associated with correlated differential privacy for autonomous driving. First, instead of transmitting raw data to the server following collection, a device that employs federated learning can perform calculations to obtain the training model at each node. Second, we specifically perform a correlated classification analysis to encrypt data that share high relevance, which can minimize the system cost. Then, correlated differential privacy is utilized to achieve the preservation of data privacy before sharing. In contrast to the traditional differential privacy, the proposed solution guarantees enhanced privacy to meet the demands of customization. The experimental results show that our scheme is more refined in terms of user heterogeneity and the utility of data than others without violating privacy.https://www.mdpi.com/1424-8220/25/1/178federated learningcorrelated differential privacyautonomous driving |
| spellingShingle | Yuan Tian Yanfeng Shi Yue Zhang Qikun Tian Personalized Federated Learning Scheme for Autonomous Driving Based on Correlated Differential Privacy Sensors federated learning correlated differential privacy autonomous driving |
| title | Personalized Federated Learning Scheme for Autonomous Driving Based on Correlated Differential Privacy |
| title_full | Personalized Federated Learning Scheme for Autonomous Driving Based on Correlated Differential Privacy |
| title_fullStr | Personalized Federated Learning Scheme for Autonomous Driving Based on Correlated Differential Privacy |
| title_full_unstemmed | Personalized Federated Learning Scheme for Autonomous Driving Based on Correlated Differential Privacy |
| title_short | Personalized Federated Learning Scheme for Autonomous Driving Based on Correlated Differential Privacy |
| title_sort | personalized federated learning scheme for autonomous driving based on correlated differential privacy |
| topic | federated learning correlated differential privacy autonomous driving |
| url | https://www.mdpi.com/1424-8220/25/1/178 |
| work_keys_str_mv | AT yuantian personalizedfederatedlearningschemeforautonomousdrivingbasedoncorrelateddifferentialprivacy AT yanfengshi personalizedfederatedlearningschemeforautonomousdrivingbasedoncorrelateddifferentialprivacy AT yuezhang personalizedfederatedlearningschemeforautonomousdrivingbasedoncorrelateddifferentialprivacy AT qikuntian personalizedfederatedlearningschemeforautonomousdrivingbasedoncorrelateddifferentialprivacy |