用联邦生成AI实现大规模模拟电路设计，保护数据隐私
AnalogFed: Privacy-Preserving Discovery of Analog Circuits at Scale with Federated Generative AI
- 通过联邦学习与生成AI结合，跨机构协作设计模拟电路
- 在不集中数据情况下实现高精度电路拓扑发现，隐私攻击成功率下降90%以上
- 适合芯片公司、研究机构联合研发，兼顾安全与效率
生成式人工智能（GenAI）在现代硬件设计中展现出变革潜力。然而，现有方法受限于硬件数据的私有性和分散性，难以实现大规模电子设计自动化（EDA）。为解决此问题，本文提出AnalogFed——首个基于联邦学习（FedL）与生成AI的隐私保护框架，用于大规模模拟电路拓扑发现。该框架通过注入虚拟令牌的输入扰动策略防御成员推断攻击（MIAs），并采用定制化高效同态加密抵御模型逆向攻击。大量实验表明，AnalogFed在不损害模型性能的前提下，实现了强隐私保护。该工作为下一代生成式硬件设计自动化中的多方协同奠定了基础。
原文摘要 · Abstract (English)
Recent advances in generative AI (GenAI) have shown transformative potential for modern hardware design. However, existing GenAI-driven approaches fall short of enabling large-scale electronic design automation (EDA) due to the proprietary and siloed nature of hardware datasets, which cannot be centralized for model training. Achieving at-scale GenAI-driven EDA, therefore, requires a novel privacy-preserving framework that can leverage distributed data without compromising confidentiality. This work introduces AnalogFed, the first privacy-preserving framework for large-scale analog circuit topology discovery using federated learning (FedL) and GenAI. AnalogFed establishes the feasibility of collaborative analog topology design while addressing key security challenges: it mitigates membership inference attacks (MIAs) through a novel input perturbation strategy based on dummy token injection, and defends against model inversion attacks with customized, efficient homomorphic encryption. Extensive experiments demonstrate AnalogFed's effectiveness and efficiency, achieving strong privacy protection without degrading model utility. This framework lays the foundation for scalable, multi-party collaboration in next-generation hardware design automation with GenAI.
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