Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Shiqiang Wang,Tiffany Tuor,Theodoros Salonidis,Kin K. Leung,Christian Makaya,Ting He,Kevin S. Chan +6 more
TLDR
In this paper, the authors consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place, and propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget.Abstract:
Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent-based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.read more
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Journal ArticleDOI
Privacy-Preserving and Low-Latency Federated Learning in Edge Computing
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Energy-Constrained D2D Assisted Federated Learning in Edge Computing
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Towards Efficient Communications in Federated Learning: A Contemporary Survey
TL;DR: This review aims to clarify the relationship between these communication problems, and focus on systematically analyzing the research progress of FL communication work from three perspectives: communication efficiency, communication environment, and communication resource allocation.
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Rebirth of Distributed AI-A Review of eHealth Research.
Manzoor Ahmed Khan,Najla Alkaabi +1 more
TL;DR: In this article, the authors discuss the potential interplay of different technologies and AI for achieving the required features of future smart city services and highlight the challenges of privacy of the data and training time.