# Privacy-Preserving Federated Action Recognition via Differentially Private Selective Tuning and Efficient Communication

arXiv: https://arxiv.org/abs/2603.21305 · HTML: https://mlsanity.com/paper/2603.21305/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 22 Mar 2026.

## Builds on (most cited first, 16 of 16)

- [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)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences](https://mlsanity.com/paper/1807.01647/) (2018)
- [Differentially Private Learning with Adaptive Clipping](https://mlsanity.com/paper/1905.03871/) (2019)
- [Gradient-Leakage Resilient Federated Learning](https://mlsanity.com/paper/2107.01154/) (2021)
- [Differentially Private Video Activity Recognition](https://mlsanity.com/paper/2306.15742/) (2023)
- [Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks](https://mlsanity.com/paper/2407.12588/) (2024)
- [Towards Efficient Active Learning of PDFA](https://mlsanity.com/paper/2206.09004/) (2022)
- [Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy](https://mlsanity.com/paper/2410.17098/) (2024)
- [Video-DPRP: A Differentially Private Approach for Visual Privacy-Preserving Video Human Activity Recognition](https://mlsanity.com/paper/2503.02132/) (2025)

## Abstract (arXiv)

Federated video action recognition enables collaborative model training without sharing raw video data, yet remains vulnerable to two key challenges: textitmodel exposure and textitcommunication overhead. Gradients exchanged between clients and the server can leak private motion patterns, while full-model synchronization of high-dimensional video networks causes significant bandwidth and communication costs. To address these issues, we propose textitFederated Differential Privacy with Selective Tuning and Efficient Communication for Action Recognition, namely textitFedDP-STECAR. Our textitFedDP-STECAR framework selectively fine-tunes and perturbs only a small subset of task-relevant layers under Differential Privacy (DP), reducing the surface of information leakage while preserving temporal coherence in video features. By transmitting only the tuned layers during aggregation, communication traffic is reduced by over 99% compared to full-model updates. Experiments on the UCF-101 dataset using the MViT-B-16x4 transformer show that textitFedDP-STECAR achieves up to 70.2% higher accuracy under strict privacy (ε=0.65) in centralized settings and 48% faster training with 73.1% accuracy in federated setups, enabling scalable and privacy-preserving video action recognition. Code available at https://github.com/izakariyya/mvit-federated-videodp