# Federated Dynamic GNN with Secure Aggregation

arXiv: https://arxiv.org/abs/2009.07351 · HTML: https://mlsanity.com/paper/2009.07351/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2020.

## Built on by (most cited first, 15 of 15)

- [Graph Unlearning](https://mlsanity.com/paper/2103.14991/) (2021)
- [Federated Graph Classification over Non-IID Graphs](https://mlsanity.com/paper/2106.13423/) (2021)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [Federated Graph Neural Networks: Overview, Techniques and Challenges](https://mlsanity.com/paper/2202.07256/) (2022)
- [Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI](https://mlsanity.com/paper/2111.06061/) (2021)
- [Federated Graph Learning -- A Position Paper](https://mlsanity.com/paper/2105.11099/) (2021)
- [FedGraph: Federated Graph Learning with Intelligent Sampling](https://mlsanity.com/paper/2111.01370/) (2021)
- [SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks](https://mlsanity.com/paper/2106.02743/) (2021)
- [More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks](https://mlsanity.com/paper/2202.03195/) (2022)
- [A Survey of Graph Unlearning](https://mlsanity.com/paper/2310.02164/) (2023)
- [FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks](https://mlsanity.com/paper/2104.07145/) (2021)
- [Graph Federated Learning for CIoT Devices in Smart Home Applications](https://mlsanity.com/paper/2212.14395/) (2022)
- [Personalized Federated Domain Adaptation for Item-to-Item Recommendation](https://mlsanity.com/paper/2306.03191/) (2023)
- [2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection](https://mlsanity.com/paper/2310.08335/) (2023)
- [Differentially Private Graph Neural Network with Importance-Grained Noise Adaption](https://mlsanity.com/paper/2308.04943/) (2023)

## Abstract (arXiv)

Given video data from multiple personal devices or street cameras, can we exploit the structural and dynamic information to learn dynamic representation of objects for applications such as distributed surveillance, without storing data at a central server that leads to a violation of user privacy? In this work, we introduce Federated Dynamic Graph Neural Network (Feddy), a distributed and secured framework to learn the object representations from multi-user graph sequences: i) It aggregates structural information from nearby objects in the current graph as well as dynamic information from those in the previous graph. It uses a self-supervised loss of predicting the trajectories of objects. ii) It is trained in a federated learning manner. The centrally located server sends the model to user devices. Local models on the respective user devices learn and periodically send their learning to the central server without ever exposing the user's data to server. iii) Studies showed that the aggregated parameters could be inspected though decrypted when broadcast to clients for model synchronizing, after the server performed a weighted average. We design an appropriate aggregation mechanism of secure aggregation primitives that can protect the security and privacy in federated learning with scalability. Experiments on four video camera datasets (in four different scenes) as well as simulation demonstrate that Feddy achieves great effectiveness and security.