分布式人工智能网络中语义通信对齐的零遗忘保持
Zero-Forget Preservation of Semantic Communication Alignment in Distributed AI Networks
AI总结：
针对分布式AI网络中域适应扭曲语义通信对齐的问题，提出零遗忘域适应框架，通过稀疏附加修改优化参数，实现对齐保持且DA性能损失小、内存成本低。
摘要：
未来通信网络有望连接大规模分布式人工智能（AI）。利用AI对的对齐先验知识，将高维数据传输转化为高度压缩的语义通信（SC）具有广阔前景。然而，为适应本地数据分布和用户偏好，AI通常会适配不同领域，这从根本上扭曲了SC对齐。本文提出零遗忘域适应（ZFDA）框架以保持SC对齐。为防止域适应（DA）改变AI的大量神经参数，我们设计了参数的稀疏附加修改（SAM），其可高效存储并关闭以恢复SC对齐。为优化SAM，我们将其解耦为易处理的连续变量和二进制掩码，再通过基于分数的优化处理二进制掩码。在图像传输SC系统上的实验评估表明，该框架完美保持SC对齐，DA性能几乎无损失，部分情况下甚至提升，额外内存成本低于1%。
英文摘要：
Future communication networks are expected to connect massive distributed artificial intelligence (AI). Exploiting aligned priori knowledge of AI pairs, it is promising to convert high-dimensional data transmission into highly-compressed semantic communications (SC). However, to accommodate the local data distribution and user preferences, AIs generally adapt to different domains, which fundamentally distorts the SC alignment. In this paper, we propose a zero-forget domain adaptation (ZFDA) framework to preserve SC alignment. To prevent the DA from changing substantial neural parameters of AI, we design sparse additive modifications (SAM) to the parameters, which can be efficiently stored and switched-off to restore the SC alignment. To optimize the SAM, we decouple it into tractable continuous variables and a binary mask, and then handle the binary mask by a score-based optimization. Experimental evaluations on a SC system for image transmissions validate that the proposed framework perfectly preserves the SC alignment with almost no loss of DA performance, even improved in some cases, at a cost of less than 1% of additional memory.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
