Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data

• URL: http://arxiv.org/abs/2312.10411v1
• Date: Sat, 16 Dec 2023 10:35:06 GMT
• Title: Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data
• Authors: Youssra Cheriguene, Wael Jaafar, Halim Yanikomeroglu, Chaker Abdelaziz Kerrache
• Abstract summary: 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.
• Score: 22.775113283662883
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this paper a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.

Related papers

• Towards Robust Federated Learning via Logits Calibration on Non-IID Data [49.286558007937856]
  Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. Recent studies have shown that FL is vulnerable to adversarial examples, leading to a significant drop in its performance. In this work, we adopt the adversarial training (AT) framework to improve the robustness of FL models against adversarial example (AE) attacks.
  arXiv  Detail & Related papers  (2024-03-05T09:18:29Z)
• FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning [98.43475653490219]
  Federated learning (FL) is susceptible to poisoning attacks. FreqFed is a novel aggregation mechanism that transforms the model updates into the frequency domain. We demonstrate that FreqFed can mitigate poisoning attacks effectively with a negligible impact on the utility of the aggregated model.
  arXiv  Detail & Related papers  (2023-12-07T16:56:24Z)
• Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimization [16.265792031520945]
  Federated learning (FL) involves several devices that collaboratively train a shared model without transferring their local data. FL reduces the communication overhead, making it a promising learning method in UAV-enhanced wireless networks with scarce energy resources. Despite the potential, implementing FL in UAV-enhanced networks is challenging, as conventional UAV placement methods that maximize coverage increase the FL delay.
  arXiv  Detail & Related papers  (2023-08-31T17:50:54Z)
• Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning [18.93536585798473]
  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)
  arXiv  Detail & Related papers  (2023-08-14T17:00:13Z)
• 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)
• Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones [57.468730437381076]
  We present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. There are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules. We propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule.
  arXiv  Detail & Related papers  (2022-01-03T16:49:33Z)
• 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)
• Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning [98.05061014090913]
  Federated learning (FL) emerges as a popular distributed learning schema that learns from a set of participating users without requiring raw data to be shared. adversarial training (AT) provides a sound solution for centralized learning, extending its usage for FL users has imposed significant challenges. We show that existing FL techniques cannot effectively propagate adversarial robustness among non-iid users. We propose a simple yet effective propagation approach that transfers robustness through carefully designed batch-normalization statistics.
  arXiv  Detail & Related papers  (2021-06-18T15:52:33Z)
• 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.