Wang, Yining and Ni, Wanli and Yi, Wenqiang and Xu, Xiaodong and Zhang, Ping and Nallanathan, Arumugam (2024) Federated Contrastive Learning for Personalized Semantic Communication. IEEE Communications Letters, 28 (8). pp. 1875-1879. DOI https://doi.org/10.1109/lcomm.2024.3415431
Wang, Yining and Ni, Wanli and Yi, Wenqiang and Xu, Xiaodong and Zhang, Ping and Nallanathan, Arumugam (2024) Federated Contrastive Learning for Personalized Semantic Communication. IEEE Communications Letters, 28 (8). pp. 1875-1879. DOI https://doi.org/10.1109/lcomm.2024.3415431
Wang, Yining and Ni, Wanli and Yi, Wenqiang and Xu, Xiaodong and Zhang, Ping and Nallanathan, Arumugam (2024) Federated Contrastive Learning for Personalized Semantic Communication. IEEE Communications Letters, 28 (8). pp. 1875-1879. DOI https://doi.org/10.1109/lcomm.2024.3415431
Abstract
In this letter, we design a federated contrastive learning (FedCL) framework aimed at supporting personalized semantic communication. Our FedCL enables collaborative training of local semantic encoders across multiple clients and a global semantic decoder owned by the base station. This framework supports heterogeneous semantic encoders since it does not require client-side model aggregation. Furthermore, to tackle the semantic imbalance issue arising from heterogeneous datasets across distributed clients, we employ contrastive learning to train a semantic centroid generator (SCG). This generator obtains representative global semantic centroids that exhibit intra-semantic compactness and inter-semantic separability. Consequently, it provides superior supervision for learning discriminative local semantic features. Additionally, we conduct theoretical analysis to quantify the convergence performance of FedCL. Simulation results verify the superiority of the proposed FedCL framework compared to other distributed learning benchmarks in terms of task performance and robustness under different numbers of clients and channel conditions, especially in low signal-to-noise ratio and highly heterogeneous data scenarios.
Item Type: | Article |
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Uncontrolled Keywords: | Federated semantic learning; contrastive learning; task-oriented communications; data heterogeneity |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 04 Jul 2024 10:22 |
Last Modified: | 30 Oct 2024 20:47 |
URI: | http://repository.essex.ac.uk/id/eprint/38717 |
Available files
Filename: Accepted_Manuscript.pdf