# AnalogFed: Privacy-Preserving Discovery of Analog Circuits at Scale with Federated Generative AI

arXiv: https://arxiv.org/abs/2507.15104 · HTML: https://mlsanity.com/paper/2507.15104/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 42 of its 71 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 30 of 42)

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- [ChipNeMo: Domain-Adapted LLMs for Chip Design](https://mlsanity.com/paper/2311.00176/) (2023)
- [FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning](https://mlsanity.com/paper/2309.00363/) (2023)
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- [Deep Learning with Gaussian Differential Privacy](https://mlsanity.com/paper/1911.11607/) (2019)
- [The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance](https://mlsanity.com/paper/2401.03729/) (2024)
- [A Systematic Survey on Deep Generative Models for Graph Generation](https://mlsanity.com/paper/2007.06686/) (2020)
- [VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool](https://mlsanity.com/paper/2408.08927/) (2024)
- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods](https://mlsanity.com/paper/2406.15968/) (2024)
- [CktGNN: Circuit Graph Neural Network for Electronic Design Automation](https://mlsanity.com/paper/2308.16406/) (2023)
- [Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models](https://mlsanity.com/paper/2201.12675/) (2022)
- [AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs](https://mlsanity.com/paper/2001.01808/) (2020)

## Abstract (arXiv)

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.