Yunming Liao

Orcid: 0000-0002-5065-2600

According to our database1, Yunming Liao authored at least 17 papers between 2022 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Overcoming Noisy Labels and Non-IID Data in Edge Federated Learning.
IEEE Trans. Mob. Comput., December, 2024

Asynchronous Decentralized Federated Learning for Heterogeneous Devices.
IEEE/ACM Trans. Netw., October, 2024

Ferrari: A Personalized Federated Learning Framework for Heterogeneous Edge Clients.
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

YOGA: Adaptive Layer-Wise Model Aggregation for Decentralized Federated Learning.
IEEE/ACM Trans. Netw., April, 2024

Accelerating Federated Learning With Data and Model Parallelism in Edge Computing.
IEEE/ACM Trans. Netw., February, 2024

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices.
CoRR, 2024

Collaborative Inference for Large Models with Task Offloading and Early Exiting.
CoRR, 2024

ParallelSFL: A Novel Split Federated Learning Framework Tackling Heterogeneity Issues.
Proceedings of the 30th Annual International Conference on Mobile Computing and Networking, 2024

Accelerating Hierarchical Federated Learning with Model Splitting in Edge Computing.
Proceedings of the 30th IEEE International Conference on Parallel and Distributed Systems, 2024

MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation.
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

Federated Semi-Supervised Learning with Local and Global Updating Frequency Optimization.
Proceedings of the 24th IEEE International Symposium on Cluster, 2024

2023
Adaptive Control of Local Updating and Model Compression for Efficient Federated Learning.
IEEE Trans. Mob. Comput., October, 2023

Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning.
Proceedings of the IEEE INFOCOM 2023, 2023

2022
Decentralized Federated Learning with Data Feature Transmission and Neighbor Selection.
Proceedings of the 28th IEEE International Conference on Parallel and Distributed Systems, 2022

Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing.
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022


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