  Despite the great success achieved, existing video moment retrieval (VMR)
methods are developed under the assumption that data are centralizedly stored.
However, in real-world applications, due to the inherent nature of data
generation and privacy concerns, data are often distributed on different silos,
bringing huge challenges to effective large-scale training. In this work, we
try to overcome above limitation by leveraging the recent success of federated
learning. As the first that is explored in VMR field, the new task is defined
as video moment retrieval with distributed data. Then, a novel federated
learning method named FedVMR is proposed to facilitate large-scale and secure
training of VMR models in decentralized environment. Experiments on benchmark
datasets demonstrate its effectiveness. This work is the very first attempt to
enable safe and efficient VMR training in decentralized scene, which is hoped
to pave the way for further study in the related research field.
