  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).
