FedVideoMAE: Efficient Privacy-Preserving Federated Video Moderation

ArXi:2512.18809v2 Announce Type: replace-cross Short-form video moderation increasingly needs learning pipelines that protect user privacy without paying the full bandwidth and latency cost of cloud-centralized inference. We present FedVideoMAE, an on-device federated framework for video violence detection that combines self-supervised VideoMAE representations, LoRA-based parameter-efficient adaptation, client-side DP-SGD, and server-side secure aggregation.