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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 datageneration and privacy concerns, data are often distributed on different silos,bringing huge challenges to effective large-scale training. In this work, wetry to overcome above limitation by leveraging the recent success of federatedlearning. As the first that is explored in VMR field, the new task is definedas video moment retrieval with distributed data. Then, a novel federatedlearning method named FedVMR is proposed to facilitate large-scale and securetraining of VMR models in decentralized environment. Experiments on benchmarkdatasets demonstrate its effectiveness. This work is the very first attempt toenable safe and efficient VMR training in decentralized scene, which is hopedto pave the way for further study in the related research field.

FedVMR: A New Federated Learning method for Video Moment Retrieval