Federated Learning for Secure Sensor Cloud
type: CreativeWork
description: Intelligent sensing solutions bridge the gap between the physical world and the cyber world by digitizing the sensor data collected from sensor devices. Sensor cloud networks provide resources to physical and virtual sensing devices and enable uninterrupted intelligent solutions to end-users. Thanks to advancements in machine learning algorithms and big data, the automation of mundane tasks with artificial intelligence is becoming a more reliable smart option. However, existing approaches based on centralized Machine Learning (ML) on sensor cloud networks fail to ensure data privacy. Moreover, centralized ML works with the pre-requisite to have the entire training dataset from end-devices transferred to a central server. We propose a Federated Learning (FL) based approach to ensure data privacy on end-devices in a sensor cloud network. Microservices of our approach provides software as a service implementation of FL with instances of cloud servers such as amazon web services (AWS). Our framework enables a personalized version of FL implementation. The framework is built using pure python APIs, which allows simple implementation and helps to skip learning of brand new APIs of an FL framework. Our proposed framework enhances privacy and security with cryptosystem tools to obfuscate the information of the FL process from unauthorized access.
abstract: Intelligent sensing solutions bridge the gap between the physical world and the cyber world by digitizing the sensor data collected from sensor devices. Sensor cloud networks provide resources to physical and virtual sensing devices and enable uninterrupted intelligent solutions to end-users. Thanks to advancements in machine learning algorithms and big data, the automation of mundane tasks with artificial intelligence is becoming a more reliable smart option. However, existing approaches based on centralized Machine Learning (ML) on sensor cloud networks fail to ensure data privacy. Moreover, centralized ML works with the pre-requisite to have the entire training dataset from end-devices transferred to a central server. We propose a Federated Learning (FL) based approach to ensure data privacy on end-devices in a sensor cloud network. Microservices of our approach provides software as a service implementation of FL with instances of cloud servers such as amazon web services (AWS). Our framework enables a personalized version of FL implementation. The framework is built using pure python APIs, which allows simple implementation and helps to skip learning of brand new APIs of an FL framework. Our proposed framework enhances privacy and security with cryptosystem tools to obfuscate the information of the FL process from unauthorized access.
author: Mothukuri, Viraaji
keywords: Federated Learning, Decentralized machine learning, Sensor cloud, Quantization, Security, Cryptosystem., Data Science, Software Engineering
date published: 2021-05-10T07:00:00Z
date modified: 2026-07-24T02:43:51Z
provider: Kennesaw State University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Kennesaw State University