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.

本文提出了一种分布式和安全的 Feddy 框架，用于从多用户图形序列中学习对象表示，并使用安全聚合机制来保护联邦学习中的安全性和隐私性，实验证明 Feddy 在四个视频摄像头数据集上达到了很高的效果和安全性。

具有安全聚合的联邦动态 GNN

Federated Dynamic GNN with Secure Aggregation

This paper presents a scalable Bayesian technique for decentralized state
estimation from multiple platforms in dynamic environments. As has long been
recognized, centralized architectures impose severe scaling limitations for
distributed systems due to the enormous communication overheads. We propose a
strictly decentralized approach in which only nearby platforms exchange
information. They do so through an interactive communication protocol aimed at
maximizing information flow. Our approach is evaluated in the context of a
distributed surveillance scenario that arises in a robotic system for playing
the game of laser tag. Our results, both from simulation and using physical
robots, illustrate an unprecedented scaling capability to large teams of
vehicles.

本文介绍了一种可扩展的贝叶斯技术，用于在动态环境中从多个平台进行去中心化状态估计。通过严格分散的方法并通过互动通信协议促进信息流，实现仅在附近平台之间交换信息和在分布式监视方案的上下文下进行评估，其结果展示了对大型车队的前所未有的可扩展能力。