Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning

• URL: http://arxiv.org/abs/2308.07273v1
• Date: Mon, 14 Aug 2023 17:00:13 GMT
• Title: Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning
• Authors: Youssra Cheriguene, Wael Jaafar, Chaker Abdelaziz Kerrache, Halim Yanikomeroglu, Fatima Zohra Bousbaa, and Nasreddine Lagraa
• Abstract summary: Unmanned aerial vehicle (UAV)-enabled edge federated learning (FL) has sparked a rise in research interest. Due to the redundancy of UAV collected data, e.g., imaging data, and non-rigorous FL participant selection, the convergence time of the FL learning process and bias of the FL model may increase. We propose a novel UAV participant selection scheme, called data-efficient energy-aware participant selection strategy (DEEPS)
• Score: 18.93536585798473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unmanned aerial vehicle (UAV)-enabled edge federated learning (FL) has sparked a rise in research interest as a result of the massive and heterogeneous data collected by UAVs, as well as the privacy concerns related to UAV data transmissions to edge servers. However, due to the redundancy of UAV collected data, e.g., imaging data, and non-rigorous FL participant selection, the convergence time of the FL learning process and bias of the FL model may increase. Consequently, we investigate in this paper the problem of selecting UAV participants for edge FL, aiming to improve the FL model's accuracy, under UAV constraints of energy consumption, communication quality, and local datasets' heterogeneity. We propose a novel UAV participant selection scheme, called data-efficient energy-aware participant selection strategy (DEEPS), which consists of selecting the best FL participant in each sub-region based on the structural similarity index measure (SSIM) average score of its local dataset and its power consumption profile. Through experiments, we demonstrate that the proposed selection scheme is superior to the benchmark random selection method, in terms of model accuracy, training time, and UAV energy consumption.

Related papers

• Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data [22.775113283662883]
  Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively. FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server. We propose in this paper a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores.
  arXiv  Detail & Related papers  (2023-12-16T10:35:06Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning [52.7230652428711]
  Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
  arXiv  Detail & Related papers  (2023-06-05T16:01:33Z)
• DPP-based Client Selection for Federated Learning with Non-IID Data [97.1195165400568]
  This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) We first analyze the effect of CS in FL and show that FL training can be accelerated by adequately choosing participants to diversify the training dataset in each round of training. We leverage data profiling and determinantal point process (DPP) sampling techniques to develop an algorithm termed Federated Learning with DPP-based Participant Selection (FL-DP$3$S)
  arXiv  Detail & Related papers  (2023-03-30T13:14:54Z)
• A Survey on Participant Selection for Federated Learning in Mobile Networks [47.88372677863646]
  Federated Learning (FL) is an efficient distributed machine learning paradigm that employs private datasets in a privacy-preserving manner. Due to limited communication bandwidth and unstable availability of such devices in a mobile network, only a fraction of end devices can be selected in each round.
  arXiv  Detail & Related papers  (2022-07-08T04:22:48Z)
• Federated Learning Over Cellular-Connected UAV Networks with Non-IID Datasets [19.792426676330212]
  Federated learning (FL) is a promising distributed learning technique. This paper proposes a new FL model over a cellular-connected unmanned aerial vehicle (UAV) network. We propose a tractable analytical framework of the uplink outage probability in the cellular-connected UAV network.
  arXiv  Detail & Related papers  (2021-10-13T23:15:20Z)
• UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning [4.273341750394231]
  Unmanned aerial vehicles (UAVs) have emerged as a promising solution for data collection of wireless sensor networks (WSNs) We aim to minimize the total energy consumption of the UAV-WSN system in a complete round of data collection. We propose a novel deep reinforcement learning (DRL) technique, pointer network-A* (Ptr-A*), which can efficiently learn from experiences of the UAV trajectory policy.
  arXiv  Detail & Related papers  (2021-08-01T03:02:11Z)
• A Principled Approach to Data Valuation for Federated Learning [73.19984041333599]
  Federated learning (FL) is a popular technique to train machine learning (ML) models on decentralized data sources. The Shapley value (SV) defines a unique payoff scheme that satisfies many desiderata for a data value notion. This paper proposes a variant of the SV amenable to FL, which we call the federated Shapley value.
  arXiv  Detail & Related papers  (2020-09-14T04:37:54Z)
• Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms [98.78553146823829]
  Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks. In this paper, a novel framework is proposed to implement distributed learning (FL) algorithms within a UAV swarm.
  arXiv  Detail & Related papers  (2020-02-19T14:04:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.