# FedOT：面向联邦LDMs的所有权验证与泄漏追踪水印方法

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-22 08:00
- AIHOT 分数：48
- AIHOT 链接：https://aihot.virxact.com/items/cmqrjiayl0kt9slp5ky6mdvsl
- 原文链接：https://arxiv.org/abs/2606.22875

## AI 摘要

联邦学习（FL）中的潜扩散模型（LDM）面临恶意客户端未经授权分发或转售全局模型的风险。现有基于VAE的水印方法无法追踪具体违规客户端，且可通过替换解码器轻易移除。为此提出FedOT框架，设计分块水印：第一部分用于所有权验证，第二部分用于客户端身份识别；同时引入潜向量变换（LVT），修改VAE原始潜分布以强化VAE与U-Net潜空间连接，使任何替换VAE去除水印的尝试都会导致图像质量严重下降，令模型不可用。实验表明FedOT在所有权验证和可追溯性上均取得优异性能。

## 正文

Training Latent Diffusion Models (LDMs) within Federated Learning (FL) has attracted increasing attention due to its ability to combine the powerful generative capacity of LDMs with the privacy-preserving properties of FL. However, FL requires sharing the global model with multiple participants, which risks unauthorized model distribution or resale by malicious clients. While an intuitive approach is to adopt existing VAE-based watermarking techniques for LDMs in FL, this strategy falls short in addressing such threats due to two fundamental challenges: (1) Existing methods support ownership verification but lack the ability to trace model leakage to a specific malicious client; (2) VAE-based watermarks are vulnerable, as they can be removed simply by replacing the decoder with a clean counterpart. In this paper, we propose FedOT, the first framework for ownership verification and leakage tracing in federated LDMs. Specifically, to address the first challenge, we design a chunked watermark, where the first part is for ownership verification, and the second part is used for client identification. Furthermore, to overcome the second challenge and secure the model against VAE replacement attack, we introduce Latent Vector Transformation (LVT), which strengthens the connection between the VAE and U-Net latent spaces by modifying the original latent distribution of the VAE. Consequently, any attempt to replace the VAE for watermark removal leads to significant image quality degradation, making the LDM model unusable. Extensive experiments demonstrate that FedOT achieves superior performance in both ownership verification and traceability. Project page: https://spyzixuan.github.io/FedOT/.