FedGenSC：具有信道自适应能力的联邦生成式语义通信
FedGenSC: Federated Generative Semantic Communication with Channel-Aware Adaptation
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中文总结 AI 辅助
针对联邦语义通信中判别器聚合不稳定、语义漂移和信道不可知三大问题，提出FedGenSC框架，通过全局生成器、语义原型库和SNR条件生成，在非IID数据下显著提升性能。
中文摘要 AI 辅助
将生成对抗网络（GAN）集成到联邦语义通信（SemCom）中是一种自然的演进，因为生成先验可以在判别式解码器无法应对的信道失真下恢复语义保真度。然而，据我们所知，先前的文献既未识别也未解决朴素GAN联邦化引入的三种失败模式：在非独立同分布（non-IID）数据下的判别器聚合不稳定性、由发散局部嵌入空间引起的语义漂移，以及无法适应异构链路条件的与信道无关的生成。我们提出了联邦生成式语义通信（FedGenSC），通过采用全局生成器与仅本地判别器、通过语义原型库提供跨客户端语义信息，以及基于瞬时信噪比（SNR）调节生成过程，来缓解所有这三种问题。在Rayleigh衰落信道（K=10个客户端，Dirichlet α=0.5）上的Europarl数据集实验中，FedGenSC在非IID数据下，在测试的SNR范围内优于FedDeepSC基线，在18 dB时双语评估替补（BLEU）-1相对改进高达58.2%。消融研究证实了每个组件的独立贡献。
英文摘要
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 α=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.
发表机构
- American University of Beirut（贝鲁特美国大学）
机构由 AI 辅助整理，请以论文原文为准。
