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.

翻译：近年来，生成式人工智能（GenAI）在先进硬件设计中展现出变革性潜力。然而，由于硬件数据集的专有性与孤岛特性——此类数据无法集中化用于模型训练——现有基于GenAI的方法难以实现大规模电子设计自动化（EDA）。为实现规模化GenAI驱动的EDA，亟需一种既能利用分布式数据又不损害机密性的新型隐私保护框架。本文提出AnalogFed，这是首个基于联邦学习（FedL）与GenAI的隐私保护大规模模拟电路拓扑发现框架。AnalogFed在解决关键安全挑战的同时，验证了协作式模拟拓扑设计的可行性：其通过基于伪令牌注入的新型输入扰动策略，有效缓解成员推断攻击（MIAs）；并采用定制的、高效的同态加密技术防御模型反演攻击。大量实验表明，AnalogFed在实现强隐私保护的同时未降低模型效用，具备高效性和有效性。该框架为基于GenAI的下一代硬件设计自动化中的可扩展多方协作奠定了基础。