Related papers: Privatized Graph Federated Learning

Privatized Graph Federated Learning

URL: http://arxiv.org/abs/2203.07105v1
Date: Mon, 14 Mar 2022 13:48:23 GMT
Title: Privatized Graph Federated Learning
Authors: Elsa Rizk, Stefan Vlaski, Ali H. Sayed
Abstract summary: We introduce graph federated learning, which consists of multiple units connected by a graph. We show how graph homomorphic perturbations can be used to ensure the algorithm is differentially private.
Score: 57.14673504239551
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-master multi-client structure. It can also be subject to privacy attacks targeting personal information on the communication links. In this work, we introduce graph federated learning (GFL), which consists of multiple federated units connected by a graph. We then show how graph homomorphic perturbations can be used to ensure the algorithm is differentially private. We conduct both convergence and privacy theoretical analyses and illustrate performance by means of computer simulations.

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