Reference : Efficient Federated Learning Algorithm for Resource Allocation in Wireless IoT Networks

	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	To cite this reference:	http://hdl.handle.net/10993/44598

Title :	Efficient Federated Learning Algorithm for Resource Allocation in Wireless IoT Networks
Language :	English
Author, co-author :	Nguyen, van Dinh [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
	Sharma, Shree Krishna [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
	Vu, Thang Xuan [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
	Chatzinotas, Symeon [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
	Ottersten, Björn [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	In press
Journal title :	IEEE Internet of Things Journal
Publisher :	Institute of Electrical and Electronics Engineers
Peer reviewed :	Yes
Audience :	International
ISSN :	2327-4662
Keywords :	[en] Energy-efficiency ; IoT ; federated learning
Abstract :	[en] Federated learning (FL) allows multiple edge computing nodes to jointly build a shared learning model without having to transfer their raw data to a centralized server, thus reducing communication overhead. However, FL still faces a number of challenges such as non-iid distributed data and heterogeneity of user equipments (UEs). Enabling a large number of UEs to join the training process in every round raises a potential issue of the heavy global communication burden. To address these issues, we generalize the current state-of-the-art Federated Averaging (FedAvg) by adding a weight-based proximal term to the local loss function. The proposed FL algorithm runs stochastic gradient descent in parallel on a sampled subset of the total UEs with replacement during each global round. We provide a convergence upper bound characterizing the trade-off between convergence rate and global rounds, showing that a small number of active UEs per round still guarantees convergence. Next, we employ the proposed FL algorithm in wireless Internet-of-Things (IoT) networks to minimize either total energy consumption or completion time of FL, where a simple yet efficient path-following algorithm is developed for its solutions. Finally, numerical results on unbalanced datasets are provided to demonstrate the performance improvement and robustness on the convergence rate of the proposed FL algorithm over FedAvg. They also reveal that the proposed algorithm requires much less training time and energy consumption than the FL algorithm with full user participation. These observations advocate the proposed FL algorithm for a paradigm shift in bandwidth- constrained learning wireless IoT networks.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Funders :	European Research Council
Name of the research project :	the ERC project AGNOSTIC
Permalink :	http://hdl.handle.net/10993/44598
DOI :	10.1109/JIOT.2020.3022534
Framework Programme / European Project :	H2020 ; 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems

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	Fulltext file(s): File Commentary Version Size Access Open access FL for Wireless IoT Network_Final version.pdf Author postprint 2.06 MB View/Open

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