A trust and energy-aware double deep reinforcement learning scheduling strategy for federated learning on IoT devices

Gaith Rjoub, Omar Abdel Wahab, Jamal Bentahar, Ahmed Bataineh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

Federated learning is a revolutionary machine learning approach whose main idea is to train the machine learning model in a distributed fashion over a large number of edge/end devices without having to share the raw data. We consider in this work a federated learning scenario wherein the local training is carried out on IoT devices and the global aggregation is done at the level of an edge server. One essential challenge in this emerging approach is scheduling, i.e., how to select the IoT devices to participate in the distributed training process. The existing approaches suggest to base the scheduling decision on the resource characteristics of the devices to guarantee that the selected devices would have enough resources to carry out the training. In this work, we argue that trust should be an integral part of the decision-making process and therefore design a trust establishment mechanism between the edge server and IoT devices. The trust mechanism aims to detect those IoT devices that over-utilize or under-utilize their resources during the local training. Thereafter, we design a Double Deep Q Learning (DDQN)-based scheduling algorithm that takes into account the trust scores and energy levels of the IoT devices to make appropriate scheduling decisions. Experiments conducted using a real-world dataset (https://www.cs.toronto.edu/~kriz/cifar.html) show that our DDQN solution always achieves better performance compared to the DQN and random scheduling algorithms.

Original languageBritish English
Title of host publicationService-Oriented Computing - 18th International Conference, ICSOC 2020, Proceedings
EditorsEleanna Kafeza, Boualem Benatallah, Fabio Martinelli, Hakim Hacid, Athman Bouguettaya, Hamid Motahari
PublisherSpringer Science and Business Media Deutschland GmbH
Pages319-333
Number of pages15
ISBN (Print)9783030653095
DOIs
StatePublished - 2020
Event18th International Conference on Service-Oriented Computing, ICSOC 2020 - Dubai, United Arab Emirates
Duration: 14 Dec 202017 Dec 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12571 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Service-Oriented Computing, ICSOC 2020
Country/TerritoryUnited Arab Emirates
CityDubai
Period14/12/2017/12/20

Keywords

  • Double Deep Q-Learning (DDQN)
  • Edge computing
  • Federated learning
  • Internet of Things (IoT)
  • IoT Selection
  • Trust

Fingerprint

Dive into the research topics of 'A trust and energy-aware double deep reinforcement learning scheduling strategy for federated learning on IoT devices'. Together they form a unique fingerprint.

Cite this