Federated Learning (FL) is a privacy-constrained decentralized machine learning paradigm in which clients enable collaborative training without compromising private data. However, how to learn a robust global model in the data-heterogeneous and model-heterogeneous FL scenarios is challenging. To address it, we resort to data-free knowledge distillation to propose a new FL method (namely DFRD). DFRD equips a conditional generator on the server to approximate the training space of the local models uploaded by clients, and systematically investigates its training in terms of fidelity, transferability} and diversity. To overcome the catastrophic forgetting of the global model caused by the distribution shifts of the generator across communication rounds, we maintain an exponential moving average copy of the generator on the server. Additionally, we propose dynamic weighting and label sampling to accurately extract knowledge from local models. Finally, our extensive experiments on various image classification tasks illustrate that DFRD achieves significant performance gains compared to SOTA baselines.

基于数据异构和模型异构的联邦学习场景中，使用无数据的知识蒸馏机制提出了DFRD方法，在服务器上通过条件生成器逼近客户端上传的本地模型训练空间，并通过动态加权和标签采样准确提取本地模型的知识，实验证明DFRD相较于基准模型取得了显著的性能提升。

DFRD：无数据的异构联邦学习鲁棒性蒸馏