Heterogeneous Federated Learning (HtFL) enables collaborative learning on
multiple clients with different model architectures while preserving privacy.
Despite recent research progress, knowledge sharing in HtFL is still difficult
due to data and model heterogeneity. To tackle this issue, we leverage the
knowledge stored in pre-trained generators and propose a new upload-efficient
knowledge transfer scheme called Federated Knowledge-Transfer Loop (FedKTL).
Our FedKTL can produce client-task-related prototypical image-vector pairs via
the generator's inference on the server. With these pairs, each client can
transfer pre-existing knowledge from the generator to its local model through
an additional supervised local task. We conduct extensive experiments on four
datasets under two types of data heterogeneity with 14 kinds of models
including CNNs and ViTs. Results show that our upload-efficient FedKTL
surpasses seven state-of-the-art methods by up to 7.31% in accuracy. Moreover,
our knowledge transfer scheme is applicable in scenarios with only one edge
client. Code: this https URL

通过利用预训练生成器中存储的知识，我们提出了一种新的上传高效的知识传输方案，称为联邦知识传输环（FedKTL），它能够通过服务器上生成器的推理来生成与客户任务相关的典型图像 - 向量对，每个客户端可以通过额外的受监督的本地任务将预先存在的知识从生成器传输到其本地模型。我们在 14 种模型（包括 CNN 和 ViTs）以及两种数据异构性下的四个数据集上进行了广泛的实验，结果显示我们上传高效的 FedKTL 在准确率上超过了七种现有方法的最高 7.31％。此外，我们的知识传输方案可适用于只有一个边缘客户端的场景。