FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing
Federated learning (FL) is a distributed machine learning paradigm for edge cloud computing. FL can facilitate data-driven decision-making in tactical scenarios, effectively addressing both data volume and infrastructure challenges in edge environments. However, the diversity of clients in edge clou...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-01-01
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| Series: | Defence Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914724002009 |
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| author | Kangning Yin Xinhui Ji Yan Wang Zhiguo Wang |
| author_facet | Kangning Yin Xinhui Ji Yan Wang Zhiguo Wang |
| author_sort | Kangning Yin |
| collection | DOAJ |
| description | Federated learning (FL) is a distributed machine learning paradigm for edge cloud computing. FL can facilitate data-driven decision-making in tactical scenarios, effectively addressing both data volume and infrastructure challenges in edge environments. However, the diversity of clients in edge cloud computing presents significant challenges for FL. Personalized federated learning (pFL) received considerable attention in recent years. One example of pFL involves exploiting the global and local information in the local model. Current pFL algorithms experience limitations such as slow convergence speed, catastrophic forgetting, and poor performance in complex tasks, which still have significant shortcomings compared to the centralized learning. To achieve high pFL performance, we propose FedCLCC: Federated Contrastive Learning and Conditional Computing. The core of FedCLCC is the use of contrastive learning and conditional computing. Contrastive learning determines the feature representation similarity to adjust the local model. Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling. Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms. |
| format | Article |
| id | doaj-art-29d984e866d747049fc5d2387af79b9e |
| institution | Kabale University |
| issn | 2214-9147 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Defence Technology |
| spelling | doaj-art-29d984e866d747049fc5d2387af79b9e2025-01-23T05:26:49ZengKeAi Communications Co., Ltd.Defence Technology2214-91472025-01-01438093FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computingKangning Yin0Xinhui Ji1Yan Wang2Zhiguo Wang3School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Institute of Public Security, Kash Institute of Electronics and Information Industry, Kashi, 844000, ChinaInstitute of Public Security, Kash Institute of Electronics and Information Industry, Kashi, 844000, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Corresponding author.Federated learning (FL) is a distributed machine learning paradigm for edge cloud computing. FL can facilitate data-driven decision-making in tactical scenarios, effectively addressing both data volume and infrastructure challenges in edge environments. However, the diversity of clients in edge cloud computing presents significant challenges for FL. Personalized federated learning (pFL) received considerable attention in recent years. One example of pFL involves exploiting the global and local information in the local model. Current pFL algorithms experience limitations such as slow convergence speed, catastrophic forgetting, and poor performance in complex tasks, which still have significant shortcomings compared to the centralized learning. To achieve high pFL performance, we propose FedCLCC: Federated Contrastive Learning and Conditional Computing. The core of FedCLCC is the use of contrastive learning and conditional computing. Contrastive learning determines the feature representation similarity to adjust the local model. Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling. Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.http://www.sciencedirect.com/science/article/pii/S2214914724002009Federated learningStatistical heterogeneityPersonalized modelConditional computingContrastive learning |
| spellingShingle | Kangning Yin Xinhui Ji Yan Wang Zhiguo Wang FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing Defence Technology Federated learning Statistical heterogeneity Personalized model Conditional computing Contrastive learning |
| title | FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing |
| title_full | FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing |
| title_fullStr | FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing |
| title_full_unstemmed | FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing |
| title_short | FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing |
| title_sort | fedclcc a personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing |
| topic | Federated learning Statistical heterogeneity Personalized model Conditional computing Contrastive learning |
| url | http://www.sciencedirect.com/science/article/pii/S2214914724002009 |
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