1,500 research outputs found
A Federated Filtering Framework for Internet of Medical Things
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 () and the local filtering parameter (); 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.Comment: 6 pages, 6 Figures, accepted for oral presentation in IEEE ICC 2019,
Internet of Things, Federated Learning and Perturbation theor
SAFA : a semi-asynchronous protocol for fast federated learning with low overhead
Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this paper, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in the steps of model distribution, client selection and global aggregation to mitigate the impacts of stragglers, crashes and model staleness in order to boost efficiency and improve the quality of the global model. We have conducted extensive experiments with typical machine learning tasks. The results demonstrate that the proposed protocol is effective in terms of shortening federated round duration, reducing local resource wastage, and improving the accuracy of the global model at an acceptable communication cost
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
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