Federated Learning is a distributed machine-learning environment that allows
clients to learn collaboratively without sharing private data. This is
accomplished by exchanging parameters. However, the differences in data
distributions and computing resources among clients make related studies
difficult. To address these heterogeneous problems, we propose a novel
Federated Learning method. Our method utilizes a pre-trained model as the
backbone of the local model, with fully connected layers comprising the head.
The backbone extracts features for the head, and the embedding vector of
classes is shared between clients to improve the head and enhance the
performance of the local model. By sharing the embedding vector of classes
instead of gradient-based parameters, clients can better adapt to private data,
and communication between the server and clients is more effective. To protect
privacy, we propose a privacy-preserving hybrid method that adds noise to the
embedding vector of classes. This method has a minimal effect on the
performance of the local model when differential privacy is met. We conduct a
comprehensive evaluation of our approach on a self-built vehicle dataset,
comparing it with other Federated Learning methods under non-independent
identically distributed(Non-IID).

提出一种新的联邦学习方法，利用预训练模型作为本地模型的骨架，使用完全连接的层组成头部来解决客户端数据分布和计算资源不同的问题，在客户端之间共享类别的嵌入向量，采用加噪声的隐私保护混合方法来保护隐私，最后在自建车辆数据集上进行全面评估。