Federated Learning-Based Risk-Aware Decision toMitigate Fake Task Impacts on CrowdsensingPlatforms

• URL: http://arxiv.org/abs/2101.01266v1
• Date: Mon, 4 Jan 2021 22:43:24 GMT
• Title: Federated Learning-Based Risk-Aware Decision toMitigate Fake Task Impacts on CrowdsensingPlatforms
• Authors: Zhiyan Chen, Murat Simsek, Burak Kantarci
• Abstract summary: Mobile crowdsensing (MCS) leverages distributed and non-dedicated sensing concepts by utilizing sensors in a large number of mobile smart devices. A malicious user submitting fake sensing tasks to an MCS platform may be attempting to consume resources from any number of participants' devices. A novel approach is proposed to identify fake tasks that contain a number of independent detection devices and an aggregation entity.
• Score: 9.925311092487851
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Mobile crowdsensing (MCS) leverages distributed and non-dedicated sensing concepts by utilizing sensors imbedded in a large number of mobile smart devices. However, the openness and distributed nature of MCS leads to various vulnerabilities and consequent challenges to address. A malicious user submitting fake sensing tasks to an MCS platform may be attempting to consume resources from any number of participants' devices; as well as attempting to clog the MCS server. In this paper, a novel approach that is based on horizontal federated learning is proposed to identify fake tasks that contain a number of independent detection devices and an aggregation entity. Detection devices are deployed to operate in parallel with each device equipped with a machine learning (ML) module, and an associated training dataset. Furthermore, the aggregation module collects the prediction results from individual devices and determines the final decision with the objective of minimizing the prediction loss. Loss measurement considers the lost task values with respect to misclassification, where the final decision utilizes a risk-aware approach where the risk is formulated as a function of the utility loss. Experimental results demonstrate that using federated learning-driven illegitimate task detection with a risk aware aggregation function improves the detection performance of the traditional centralized framework. Furthermore, the higher performance of detection and lower loss of utility can be achieved by the proposed framework. This scheme can even achieve 100%detection accuracy using small training datasets distributed across devices, while achieving slightly over an 8% increase in detection improvement over traditional approaches.

Related papers

• Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification [50.28867343337997]
  This work presents a TinyML-based semantic communication framework for few-shot wireless image classification. We exploit split-learning to limit the computations performed by the end-users while ensuring privacy-preserving. meta-learning overcomes data availability concerns and speeds up training by utilizing similarly trained tasks.
  arXiv  Detail & Related papers  (2024-09-03T05:56:55Z)
• INTELLECT: Adapting Cyber Threat Detection to Heterogeneous Computing Environments [0.055923945039144884]
  This paper introduces INTELLECT, a novel solution that integrates feature selection, model pruning, and fine-tuning techniques into a cohesive pipeline for the dynamic adaptation of pre-trained ML models and configurations for IDSs. We demonstrate the advantages of incorporating knowledge distillation techniques while fine-tuning, enabling the ML model to consistently adapt to local network patterns while preserving historical knowledge.
  arXiv  Detail & Related papers  (2024-07-17T22:34:29Z)
• Deep Learning-Based Approach for User Activity Detection with Grant-Free Random Access in Cell-Free Massive MIMO [0.8520624117635328]
  This paper explores the application of supervised machine learning models to tackle activity detection issues. We introduce a data-driven algorithm specifically designed for user activity detection in Cell-Free Massive Multiple-Input Multiple-Output (CF-mMIMO) networks. The results are compelling: the algorithm achieves an exceptional 99% accuracy rate, confirming its efficacy in real-world applications.
  arXiv  Detail & Related papers  (2024-06-11T11:08:33Z)
• EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning [4.140068761522124]
  We propose an EG-ConMix method based on E-GraphSAGE, incorporating a data augmentation module to fix the problem of data imbalance. EG-ConMix exhibits significant advantages in terms of training speed and accuracy for large-scale graphs.
  arXiv  Detail & Related papers  (2024-03-24T04:09:48Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Active Inference on the Edge: A Design Study [5.815300670677979]
  Active Inference (ACI) is a concept from neuroscience that describes how the brain constantly predicts and evaluates sensory information to decrease long-term surprise. We show how our ACI agent was able to quickly and traceably solve an optimization problem while fulfilling requirements.
  arXiv  Detail & Related papers  (2023-11-17T16:03:04Z)
• Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices [72.61177465035031]
  We propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy.
  arXiv  Detail & Related papers  (2023-10-21T12:07:04Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds [53.07042574352251]
  We design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework.
  arXiv  Detail & Related papers  (2020-08-12T12:03:27Z)
• Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform [0.0]
  Real-world problems such as landmine detection require multiple sources of information to reduce the uncertainty of decision-making. A novel approach to solve these problems includes distributed systems, as presented in this work. We evaluate the performance of a trained system over the distribution of samples between training and validation sets.
  arXiv  Detail & Related papers  (2020-04-11T12:05:30Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)

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.