# Federated Knowledge Distillation for Multi-Model Architectures Lithography Hotspot Detection

arXiv: https://arxiv.org/abs/2501.04066 · HTML: https://mlsanity.com/paper/2501.04066/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Federated, robust and private distillation, one of 12 directions in Knowledge distillation.
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 7 Jan 2025.

## Placed in

- Knowledge distillation / [Federated, robust and private distillation](https://mlsanity.com/distill/federated-robust-and-private-distillation/)

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [FedKD: Communication Efficient Federated Learning via Knowledge Distillation](https://mlsanity.com/paper/2108.13323/) (2021)
- [Federated Learning with Partial Model Personalization](https://mlsanity.com/paper/2204.03809/) (2022)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation](https://mlsanity.com/paper/2107.04367/) (2021)

## Built on by (most cited first, 8 of 8)

- [Open-Source Multimodal Moxin Models with Moxin-VLM and Moxin-VLA](https://mlsanity.com/paper/2512.22208/) (2025)
- [Beyond Vulnerabilities: A Survey of Adversarial Attacks as Both Threats and Defenses in Computer Vision Systems](https://mlsanity.com/paper/2508.01845/) (2025)
- [From Metaphor to Mechanism: How LLMs Decode Traditional Chinese Medicine Symbolic Language for Modern Clinical Relevance](https://mlsanity.com/paper/2503.02760/) (2025)
- [Self-Adaptive Gamma Context-Aware SSM-based Model for Metal Defect Detection](https://mlsanity.com/paper/2503.01234/) (2025)
- [BPCLIP: A Bottom-up Image Quality Assessment from Distortion to Semantics Based on CLIP](https://mlsanity.com/paper/2506.17969/) (2025)
- [Memory Management and Contextual Consistency for Long-Running Low-Code Agents](https://mlsanity.com/paper/2509.25250/) (2025)
- [Toward Causal-Visual Programming: Enhancing Agentic Reasoning in Low-Code Environments](https://mlsanity.com/paper/2509.25282/) (2025)
- [Component Adaptive Clustering for Generalized Category Discovery](https://mlsanity.com/paper/2507.01711/) (2025)

## Abstract (arXiv)

As a special type of multimedia data, Lithography Hotspot Detection (LHD) training often requires stronger privacy protection than conventional multimedia data, and federated learning provides a promising potential solution to this challenge. However, existing approaches rely solely on either parameter aggregation or Knowledge Distillation (KD), failing to fully exploit the potential of collaborative learning. To address this, we propose FedKD-hybrid, a novel framework that synergizes the strengths of both paradigms. Specifically, FedKD-hybrid utilizes a public dataset to facilitate consensus, where clients exchange both parameters of agreed-upon layers and logits. This hybrid information is aggregated to refine local models, enhancing knowledge transfer. Extensive experiments on ICCAD-2012 and real-world FAB datasets demonstrate that FedKD-hybrid consistently outperforms state-of-the-art methods in both effectiveness and robustness.