Yang Xu
Orcid: 0000-0003-0839-3892Affiliations:
- University of Science and Technology of China, School of Computer Science and Technology, Hefei, China
According to our database1,
Yang Xu
authored at least 57 papers
between 2016 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
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Online presence:
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on orcid.org
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on dl.acm.org
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Bibliography
2024
IEEE Trans. Mob. Comput., October, 2024
IEEE Trans. Mob. Comput., October, 2024
Enhancing Decentralized and Personalized Federated Learning With Topology Construction.
IEEE Trans. Mob. Comput., October, 2024
Federated Learning With Experience-Driven Model Migration in Heterogeneous Edge Networks.
IEEE/ACM Trans. Netw., August, 2024
Decentralized Federated Learning With Adaptive Configuration for Heterogeneous Participants.
IEEE Trans. Mob. Comput., June, 2024
IEEE Trans. Mob. Comput., May, 2024
Federated Learning With Client Selection and Gradient Compression in Heterogeneous Edge Systems.
IEEE Trans. Mob. Comput., May, 2024
IEEE Trans. Mob. Comput., May, 2024
IEEE Trans. Veh. Technol., April, 2024
IEEE/ACM Trans. Netw., April, 2024
Enhancing Federated Learning With Server-Side Unlabeled Data by Adaptive Client and Data Selection.
IEEE Trans. Mob. Comput., April, 2024
Computation and Communication Efficient Federated Learning With Adaptive Model Pruning.
IEEE Trans. Mob. Comput., March, 2024
FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training.
IEEE Trans. Parallel Distributed Syst., February, 2024
Adaptive Block-Wise Regularization and Knowledge Distillation for Enhancing Federated Learning.
IEEE/ACM Trans. Netw., February, 2024
IEEE/ACM Trans. Netw., February, 2024
IEEE Trans. Mob. Comput., February, 2024
IEEE Trans. Mob. Comput., January, 2024
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024
Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning.
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024
2023
Joint Model Pruning and Topology Construction for Accelerating Decentralized Machine Learning.
IEEE Trans. Parallel Distributed Syst., October, 2023
Adaptive Control of Local Updating and Model Compression for Efficient Federated Learning.
IEEE Trans. Mob. Comput., October, 2023
IEEE Trans. Mob. Comput., September, 2023
IEEE Trans. Mob. Comput., August, 2023
Accelerating Federated Learning With Cluster Construction and Hierarchical Aggregation.
IEEE Trans. Mob. Comput., July, 2023
IEEE Trans. Mob. Comput., June, 2023
IEEE Trans. Mob. Comput., 2023
IEEE Trans. Mob. Comput., 2023
CoRR, 2023
CoopFL: Accelerating federated learning with DNN partitioning and offloading in heterogeneous edge computing.
Comput. Networks, 2023
Enhanced Federated Learning with Adaptive Block-wise Regularization and Knowledge Distillation.
Proceedings of the 31st IEEE/ACM International Symposium on Quality of Service, 2023
Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning.
Proceedings of the IEEE INFOCOM 2023, 2023
Heterogeneity-Aware Federated Learning with Adaptive Client Selection and Gradient Compression.
Proceedings of the IEEE INFOCOM 2023, 2023
Enhancing Decentralized Federated Learning for Non-IID Data on Heterogeneous Devices.
Proceedings of the 39th IEEE International Conference on Data Engineering, 2023
2022
IEEE Trans. Mob. Comput., 2022
Joint Data Collection and Resource Allocation for Distributed Machine Learning at the Edge.
IEEE Trans. Mob. Comput., 2022
IEEE J. Sel. Areas Commun., 2022
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning.
CoRR, 2022
Decentralized Federated Learning with Data Feature Transmission and Neighbor Selection.
Proceedings of the 28th IEEE International Conference on Parallel and Distributed Systems, 2022
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022
Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing.
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022
FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing.
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022
2021
FedSA: A Semi-Asynchronous Federated Learning Mechanism in Heterogeneous Edge Computing.
IEEE J. Sel. Areas Commun., 2021
Communication-efficient asynchronous federated learning in resource-constrained edge computing.
Comput. Networks, 2021
Proceedings of the Wireless Algorithms, Systems, and Applications, 2021
Learning-Driven Decentralized Machine Learning in Resource-Constrained Wireless Edge Computing.
Proceedings of the 40th IEEE Conference on Computer Communications, 2021
Proceedings of the IEEE International Conference on Acoustics, 2021
2019
2018
Proceedings of the 2018 IEEE SmartWorld, 2018
Proceedings of the 15th Annual IEEE International Conference on Sensing, 2018
Proceedings of the Database Systems for Advanced Applications, 2018
Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings.
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018
2017
Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2017
Proceedings of the Wireless Algorithms, Systems, and Applications, 2017
2016
Proceedings of the Service-Oriented Computing - 14th International Conference, 2016
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016
Proceedings of the Web Technologies and Applications - 18th Asia-Pacific Web Conference, 2016