Adaptive Role Assignment and Controlled Feature Reconstruction for Federated Image Semantic Communication
Hybrid federated semantic communication enables user equipment (UEs) to contribute model updates or semantic features under a shared training framework.
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Key points
- However, heterogeneous role costs and the interaction between model aggregation and feature reconstruction (FR) remain key challenges in practical radio access network (RAN) deployment.
- To address these issues, we propose ARC-FedSC, a RAN-aware framework that coordinates UE role assignment and server-side controlled FR under a fixed communication budget.
- Within ARC-FedSC, RAN Aware Role-Bandit learns the historical contribution of UEs in different roles together with current uplink conditions, while Temporal Consistency Alignment (TCA) regulates FR according to recent model aggregation directions.
- Experiments on CIFAR-10 with time-varying RAN channels show that ARC-FedSC outperforms baselines across all five tested role configurations in the later training stage, with the best configurations achieving about 24.17 dB average PSNR over the last 20 rounds.
Sources (1)
- [1]Adaptive Role Assignment and Controlled Feature Reconstruction for Federated Image Semantic CommunicationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 01:48 PM
Hybrid federated semantic communication enables user equipment (UEs) to contribute model updates or semantic features under a shared training framework.
However, heterogeneous role costs and the interaction between model aggregation and feature reconstruction (FR) remain key challenges in practical radio access network (RAN) deployment.
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