FEDERATED DEEP LEARNING ARCHITECTURES FOR PRIVACY-PRESERVING DATA ANALYTICS

Abstract

Federated learning (FL) has well emerged as one of the practical paradigms for the purpose of collaborative model training without any form of centralizing form of sensitive data The given paper speaks about the federated deep learning structure which must support the privacy-guaranteed information analytics among the heterogeneous clients. We trade off model utility, communication overhead and provable privacy: our significant concepts are the building blocks of principles, architectural variants and privacy mechanisms: secure aggregation, differential privacy, homomorphic encryption and we trade off model utility. We define the process of experimental assessment, outline the latest empirical scrutinising’s of benchmark suites, and clarify the result of the representative experiments controlled over the presence of very-large-scale and large-scale federated settings, explain the implications of the statistical heterogeneity and adversarial leakage, and account the considerations on which the deployment is between cross-device and cross-silos. Providing an overview of the open research directions We then provide open research directions summary to make federated deep learning and scale formally sensitive, performant, and strong.