Problem
Research questions and friction points this paper is trying to address.
Federated Semantic Communication
Generative Adversarial Networks
Non-IID Data
Semantic Drift
Channel-Agnostic Generation
FedGenSC: Federated Generative Semantic Communication with Channel-Aware Adaptation
🤖 AI Summary
本文针对GAN在联邦语义通信中遇到的三个问题，提出FedGenSC方法，通过全局生成器、局部判别器及语义原型库等手段解决这些问题。
📝 Abstract
Integrating generative adversarial networks (GANs) into federated semantic communication (SemCom) is a natural progression, as generative priors can recover semantic fidelity under channel distortion that discriminative decoders cannot. However, naive GAN federation introduces three failure modes that prior work has, to the best of our knowledge, neither identified nor resolved: discriminator aggregation instability under non-independent and identically distributed (non-IID) data, semantic drift caused by divergent local embedding spaces, and channel-agnostic generation that cannot adapt to heterogeneous link conditions. We propose federated generative semantic communication (FedGenSC), which mitigates all three by employing a global generator with local-only discriminators, providing cross-client semantic information through a semantic prototype bank, and conditioning generation on the instantaneous signal-to-noise ratio (SNR). Experiments on the Europarl dataset over Rayleigh fading channels (K=10 clients, Dirichlet {\alpha}=0.5) show that FedGenSC under non-IID data outperforms the FedDeepSC baseline across the tested SNR range, achieving up to a 58.2% relative improvement in bilingual evaluation understudy (BLEU)-1 at 18 dB. Ablation studies confirm the independent contribution of each component.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Federated Generative Semantic Communication
Channel-Aware Adaptation
Semantic Prototype Bank
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