Federated Dynamic GNN with Secure Aggregation

09/15/2020
by   Meng Jiang, et al.
0

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.

READ FULL TEXT
10/19/2020

From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security

Federated learning is an improved version of distributed machine learnin...
03/30/2020

Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem

This work investigates the task of unsupervised model personalization, a...
03/24/2022

SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning

We propose SwiftAgg+, a novel secure aggregation protocol for federated ...
10/17/2020

Secure Weighted Aggregation in Federated Learning

Federated learning (FL) schemes enable multiple clients to jointly solve...
05/19/2019

Decentralized Learning with Average Difference Aggregation for Proactive Online Social Care

The Internet and the Web are being increasingly used in proactive social...
06/04/2021

SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks

Graph Neural Networks (GNNs) are the first choice methods for graph mach...