A Federated Filtering Framework for Internet of Medical Things

Abstract

Based on the dominant paradigm, all the wearable IoT devices used in the healthcare sector also known as the internet of medical things (IoMT) are resource constrained in power and computational capabilities. The IoMT devices are continuously pushing their readings to the remote cloud servers for real-time data analytics, that causes faster drainage of the device battery. Moreover, other demerits of continuous centralizing of data include exposed privacy and high latency. This paper presents a novel Federated Filtering Framework for IoMT devices which is based on the prediction of data at the central fog server using shared models provided by the local IoMT devices. The fog server performs model averaging to predict the aggregated data matrix and also computes filter parameters for local IoMT devices. Two significant theoretical contributions of this paper are the global tolerable perturbation error (T olF ) and the local filtering parameter (δ); where the former controls the decision-making accuracy due to eigenvalue perturbation and the later balances the tradeoff between the communication overhead and perturbation error of the aggregated data matrix (predicted matrix) at the fog server. Experimental evaluation based on real healthcare data demonstrates that the proposed scheme saves upto 95% of the communication cost while maintaining reasonable data privacy and low latency.

Federated Learning for Healthcare: Systematic Review and Architecture Proposal

“…Each client individually maintains the global model according to the received local updates. Sanyal et al [47] propose the use of a fog infrastructure to enable FL on constrained IoT medical devices. A fog infrastructure encompasses computational resources available in a hospital's local network, which can be used to manage complex tasks that otherwise would require cloud computing.…”

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“…Sanyal et al [38] proposed a federated filtering framework (FFF) based on the forecast of data at the central fog server using aggregated model from IoMT devices. This framework used models provided by local IoMT devices and then shared with the fog server.…”

A federated approach to Android malware classification through Perm-Maps

“…Due to its great success, the federated logic has been investigated, in the last decade, to face main issues related to the convergence process among edge and cloud infrastructures, such as data aggregation, data mobility, and services migration [10,30,38]. Also, it has been involved in many other famous application domains, such as cryptography solutions to preserve data security [36], optimization frameworks for the medical of things devices [37], and vehicular networks optimization [43].…”