  Detection models trained by one party (including server) may face severe
performance degradation when distributed to other users (clients). Federated
learning can enable multi-party collaborative learning without leaking client
data. In this paper, we focus on a special cross-domain scenario in which the
server has large-scale labeled data and multiple clients only have a small
amount of labeled data; meanwhile, there exist differences in data
distributions among the clients. In this case, traditional federated learning
methods can't help a client learn both the global knowledge of all participants
and its own unique knowledge. To make up for this limitation, we propose a
cross-domain federated object detection framework, named FedOD. The proposed
framework first performs the federated training to obtain a public global
aggregated model through multi-teacher distillation, and sends the aggregated
model back to each client for fine-tuning its personalized local model. After a
few rounds of communication, on each client we can perform weighted ensemble
inference on the public global model and the personalized local model. We
establish a federated object detection dataset which has significant background
differences and instance differences based on multiple public autonomous
driving datasets, and then conduct extensive experiments on the dataset. The
experimental results validate the effectiveness of the proposed method.
