# FedVMR: A New Federated Learning method for Video Moment Retrieval

arXiv: https://arxiv.org/abs/2210.15977 · HTML: https://mlsanity.com/paper/2210.15977/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017)
- [Localizing Moments in Video with Natural Language](https://mlsanity.com/paper/1708.01641/) (2017)
- [TALL: Temporal Activity Localization via Language Query](https://mlsanity.com/paper/1705.02101/) (2017)
- [Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding](https://mlsanity.com/paper/1604.01753/) (2016)
- [Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language](https://mlsanity.com/paper/1912.03590/) (2019)
- [SCAFFOLD: Stochastic Controlled Averaging for Federated Learning](https://mlsanity.com/paper/1910.06378/) (2019)
- [UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2203.12745/) (2022)
- [Federated Visual Classification with Real-World Data Distribution](https://mlsanity.com/paper/2003.08082/) (2020)
- [Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos](https://mlsanity.com/paper/1906.02497/) (2019)
- [Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos](https://mlsanity.com/paper/1901.06829/) (2019)
- [Memory-Guided Semantic Learning Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2201.00454/) (2022)
- [A Survey on Temporal Sentence Grounding in Videos](https://mlsanity.com/paper/2109.08039/) (2021)
- [Federated Learning Challenges and Opportunities: An Outlook](https://mlsanity.com/paper/2202.00807/) (2022)
- [Learning Sample Importance for Cross-Scenario Video Temporal Grounding](https://mlsanity.com/paper/2201.02848/) (2022)

## Built on by (most cited first, 1 of 1)

- [Video Understanding with Large Language Models: A Survey](https://mlsanity.com/paper/2312.17432/) (2023)

## Abstract (arXiv)

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.