  Federated learning is gaining popularity as a distributed machine learning
method that can be used to deploy AI-dependent IoT applications while
protecting client data privacy and security. Due to the differences of clients,
a single global model may not perform well on all clients, so the personalized
federated learning method, which trains a personalized model for each client
that better suits its individual needs, becomes a research hotspot. Most
personalized federated learning research, however, focuses on data
heterogeneity while ignoring the need for model architecture heterogeneity.
Most existing federated learning methods uniformly set the model architecture
of all clients participating in federated learning, which is inconvenient for
each client's individual model and local data distribution requirements, and
also increases the risk of client model leakage. This paper proposes a
federated learning method based on co-training and generative adversarial
networks(GANs) that allows each client to design its own model to participate
in federated learning training independently without sharing any model
architecture or parameter information with other clients or a center. In our
experiments, the proposed method outperforms the existing methods in mean test
accuracy by 42% when the client's model architecture and data distribution vary
significantly.
