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.

翻译：由一方（包括服务器）训练得到的检测模型在分发至其他用户（客户端）时，可能会面临严重的性能下降问题。联邦学习能够在不泄露客户端数据的前提下实现多方协同学习。本文聚焦于一种特殊的跨域场景：服务器拥有大规模标注数据，而多个客户端仅有少量标注数据，同时客户端之间存在数据分布差异。在此情况下，传统联邦学习方法无法使客户端同时学习所有参与方的全局知识及其自身特有的知识。为弥补这一缺陷，我们提出了一种名为FedOD的跨域联邦目标检测框架。该框架首先通过多教师蒸馏进行联邦训练，获得一个公共的全局聚合模型，并将其回传至各客户端以微调其个性化本地模型。经过数轮通信后，每个客户端可对公共全局模型与个性化本地模型进行加权集成推理。我们基于多个公开的自动驾驶数据集构建了一个具有显著背景差异与实例差异的联邦目标检测数据集，并在该数据集上开展了大量实验。实验结果验证了所提方法的有效性。