这篇论文提出了FedKT-CSD，通过预训练自编码器生成合成数据，同时提供差分隐私保护，在异质性数据下性能不输无隐私方法。
单轮联邦学习（OSFL）通过单次通信降低开销，但客户端数据分布差异大时模型质量难保证，现有合成数据方法缺乏形式化隐私保证。FedKT-CSD框架利用公开预训练自编码器作为共享潜在空间，客户端单次前向传递后计算类别条件潜在统计并传输，服务器安全聚合并添加$(ε,δ)$-差分隐私噪声后解码合成数据集。在多个数据集和异质性设置下，FedKT-CSD在隐私约束下性能与非隐私基线相匹敌甚至更优，并支持大规模客户端。
Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning
One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so without sacrificing model quality is non-trivial, particularly when client data distributions diverge. Recent work has addressed this challenge by aggregating client knowledge on the server through the construction of transferable synthetic datasets or distillates. However, most of these methods lack formal privacy guarantees, leaving a gap in jointly achieving low communication, robustness to heterogeneity, and rigorous privacy. We propose FedKT-CSD (Federated Knowledge Transfer via Collaborative Synthetic Data), a framework inspired by neural image compression that closes this gap by leveraging publicly pretrained autoencoders as a shared latent space. Each client encodes its private data in a single forward pass, computes class-conditional latent statistics, and transmits these to the server. The server aggregates these statistics via secure aggregation, adds calibrated differential privacy noise, and decodes a synthetic dataset for training a global model and further downstream tasks. This design provides formal $(\varepsilon,δ)$-differential privacy by construction, while keeping client-side computation and communication lightweight. Despite operating under privacy constraints, FedKT-CSD is competitive with and even outperforms non-private baselines across diverse datasets and heterogeneity settings, and scales to a large number of clients. Our code is available at: https://github.com/an7123/FedKT-CSD