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Federated Deep Reinforcement Learning for Task Participation in Mobile Crowdsensing

Dongare, Sumedh ; Ortiz Jimenez, Andrea Patricia ; Klein, Anja (2023)
Federated Deep Reinforcement Learning for Task Participation in Mobile Crowdsensing.
2023 IEEE Global Communications Conference. Kuala Lumpur, Malaysia (04.-08.12.2023)
Conference or Workshop Item, Bibliographie

Abstract

Mobile Crowdsensing (MCS) is a promising distributed sensing architecture that harnesses the power of sensors on mobile units (MUs) to perform sensing tasks. The MCS is a dynamic system in which the requirements of the sensing tasks, the MUs’ conditions and the available resources change over time. The performance of an MCS system depends on the selection of the MUs participating in each sensing task. However, this is not a trivial problem. An optimal task participation strategy requires non-causal knowledge about the dynamic MCS system, a requirement that cannot be fulfilled in real implementations. Moreover, centralized optimization-based approaches do not scale with increasing number of participating MUs and often ignore the MUs’ preferences. To overcome these challenges, in this paper we propose a novel multi-agent federated deep reinforcement learning algorithm (FDRL-PPO) which does not need this perfect non-causal knowledge, but instead, enables the MUs to learn their own task participation strategies based on their own conditions, available resources, and preferences. Through federated learning, the MUs share their learned strategies without disclosing sensitive information, enabling a robust and scalable task participation scheme. Numerical evaluations validate the effectiveness and efficiency of FDRL-PPO in comparison with reference schemes.

Item Type: Conference or Workshop Item
Erschienen: 2023
Creators: Dongare, Sumedh ; Ortiz Jimenez, Andrea Patricia ; Klein, Anja
Type of entry: Bibliographie
Title: Federated Deep Reinforcement Learning for Task Participation in Mobile Crowdsensing
Language: English
Date: 6 December 2023
Event Title: 2023 IEEE Global Communications Conference
Event Location: Kuala Lumpur, Malaysia
Event Dates: 04.-08.12.2023
Corresponding Links:
Abstract:

Mobile Crowdsensing (MCS) is a promising distributed sensing architecture that harnesses the power of sensors on mobile units (MUs) to perform sensing tasks. The MCS is a dynamic system in which the requirements of the sensing tasks, the MUs’ conditions and the available resources change over time. The performance of an MCS system depends on the selection of the MUs participating in each sensing task. However, this is not a trivial problem. An optimal task participation strategy requires non-causal knowledge about the dynamic MCS system, a requirement that cannot be fulfilled in real implementations. Moreover, centralized optimization-based approaches do not scale with increasing number of participating MUs and often ignore the MUs’ preferences. To overcome these challenges, in this paper we propose a novel multi-agent federated deep reinforcement learning algorithm (FDRL-PPO) which does not need this perfect non-causal knowledge, but instead, enables the MUs to learn their own task participation strategies based on their own conditions, available resources, and preferences. Through federated learning, the MUs share their learned strategies without disclosing sensitive information, enabling a robust and scalable task participation scheme. Numerical evaluations validate the effectiveness and efficiency of FDRL-PPO in comparison with reference schemes.

Uncontrolled Keywords: Open6GHub, DAAD, emergenCITY, emergenCITY_KOM
Additional Information:

IoTSN 8: Federated Learning for IoT Networks III

Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Communications Engineering
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms > Subproject C1: Network-centred perspective
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > Transfer projects
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > Transfer projects > Transfer project T2: Prediction of network load
Date Deposited: 25 Jan 2024 10:24
Last Modified: 25 Jan 2024 10:24
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