Khalifa University

About: Khalifa University is a education organization based out in Abu Dhabi, United Arab Emirates. It is known for research contribution in the topics: Computer science & Adsorption. The organization has 3752 authors who have published 10909 publications receiving 141629 citations.

Optimal User-Edge Assignment in Hierarchical Federated Learning based on Statistical Properties and Network Topology Constraints

Abstract: Learning-based applications have demonstrated practical use cases in ubiquitous environments and amplified interest in exploiting the data stored on users' mobile devices. Distributed optimization algorithms aim to leverage such distributed and diverse data to learn a global phenomena by performing training amongst participating devices and repeatedly aggregating their local models' parameters into a global model. Federated Averaging is a promising solution that allows for extending local training before aggregating the parameters, offering better communication efficiency. However, in the cases where the participants' data are strongly skewed (i.e., local distributions are different), the model accuracy can significantly drop. To face this challenge, we leverage the edge computing paradigm to design a hierarchical learning system that performs Federated Gradient Descent on the user-edge layer and Federated Averaging on the edge-cloud layer. In this hierarchical architecture, the users might be assigned to different edges, leading to different edge-level data distributions. We formalize and optimize this user-edge assignment problem to minimize classes' distribution distance between edge nodes, which enhances the Federated Averaging performance. Our experiments on multiple real datasets show that the proposed optimized assignment is tractable and leads to faster convergence of models towards a better accuracy value.

A Crowd-Sensing Framework for Allocation of Time-Constrained and Location-Based Tasks

Abstract: Thanks to the capabilities of the built-in sensors of smart devices, mobile crowd-sensing (MCS) has become a promising technique for massive data collection. In this paradigm, the service provider recruits workers (i.e., common people with smart devices) to perform sensing tasks requested by the consumers. To efficiently handle workers’ recruitment and task allocation, several factors have to be considered such as the quality of the sensed data that the workers can deliver and the different tasks locations. This allocation becomes even more challenging when the MCS tries to efficiently allocate multiple tasks under limited budget, time constraints, and the uncertainty that selected workers will not be able to perform the tasks. In this paper, we propose a service computing framework for time constrained-task allocation in location based crowd-sensing systems. This framework relies on (1) a recruitment algorithm that implements a multi-objective task allocation algorithm based on Particle Swarm Optimization, (2) queuing schemes to handle efficiently the incoming sensing tasks in the server side and at the end-user side, (3) a task delegation mechanism to avoid delaying or declining the sensing requests due to unforeseen user context, and (4) a reputation management component to manage the reputation of users based on their sensing activities and task delegation. The platform goal is to efficiently determine the most appropriate set of workers to assign to each incoming task so that high quality results are returned within the requested response time. Simulations are conducted using real datasets from Foursquare1 and Enron email social network.2 Simulation results show that the proposed framework maximizes the aggregated quality of information, reduces the budget and response time to perform a task and increases the average recommenders’ reputation and their payment.