Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain

• URL: http://arxiv.org/abs/2407.19428v1
• Date: Sun, 28 Jul 2024 08:34:27 GMT
• Title: Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain
• Authors: Weiliang Chen, Li Jia, Yang Zhou, Qianqian Ren,
• Abstract summary: Federated learning combined with blockchain empowers secure data sharing in autonomous driving applications. The lack of data quality audits raises concerns about multi-party mistrust in trajectory prediction tasks. This paper proposes an asynchronous federated learning data sharing method based on an interpretable reputation quantization mechanism.
• Score: 8.99791083863972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning combined with blockchain empowers secure data sharing in autonomous driving applications. Nevertheless, with the increasing granularity and complexity of vehicle-generated data, the lack of data quality audits raises concerns about multi-party mistrust in trajectory prediction tasks. In response, this paper proposes an asynchronous federated learning data sharing method based on an interpretable reputation quantization mechanism utilizing graph neural network tools. Data providers share data structures under differential privacy constraints to ensure security while reducing redundant data. We implement deep reinforcement learning to categorize vehicles by reputation level, which optimizes the aggregation efficiency of federated learning. Experimental results demonstrate that the proposed data sharing scheme not only reinforces the security of the trajectory prediction task but also enhances prediction accuracy.

Related papers

• Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data [0.3683202928838613]
  Federated learning allows multiple clients to collaboratively train a shared model while maintaining data privacy. The proposed framework utilizes transfer learning, pre-training deep neural networks on a central server, and fine-tuning on clients to enhance model accuracy and adaptability. Practical applications include Car2X event predictions, where a shared model is collectively trained to recognize traffic patterns, aiding in tasks such as traffic density assessment and accident detection.
  arXiv  Detail & Related papers  (2024-09-19T08:52:49Z)
• On the Federated Learning Framework for Cooperative Perception [28.720571541022245]
  Federated learning offers a promising solution by enabling data privacy-preserving collaborative enhancements in perception, decision-making, and planning among connected and autonomous vehicles. This study introduces a specialized federated learning framework for CP, termed the federated dynamic weighted aggregation (FedDWA) algorithm. This framework employs dynamic client weighting to direct model convergence and integrates a novel loss function that utilizes Kullback-Leibler divergence (KLD) to counteract detrimental effects of non-independently and identically distributed (Non-IID) and unbalanced data.
  arXiv  Detail & Related papers  (2024-04-26T04:34:45Z)
• Ungeneralizable Examples [70.76487163068109]
  Current approaches to creating unlearnable data involve incorporating small, specially designed noises. We extend the concept of unlearnable data to conditional data learnability and introduce textbfUntextbfGeneralizable textbfExamples (UGEs) UGEs exhibit learnability for authorized users while maintaining unlearnability for potential hackers.
  arXiv  Detail & Related papers  (2024-04-22T09:29:14Z)
• CONVERT:Contrastive Graph Clustering with Reliable Augmentation [110.46658439733106]
  We propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT) In our method, the data augmentations are processed by the proposed reversible perturb-recover network. To further guarantee the reliability of semantics, a novel semantic loss is presented to constrain the network.
  arXiv  Detail & Related papers  (2023-08-17T13:07:09Z)
• Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity [60.791736094073]
  Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks. We propose a magnitude-driven sparsification scheme, which addresses the non-convergence issue of SIGNSGD. The proposed scheme is validated through experiments on Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets.
  arXiv  Detail & Related papers  (2023-02-19T17:42:35Z)
• Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping [77.53019031244908]
  We present a novel decentralized learning framework based on nonlinear gossiping (NGO), that enjoys an appealing finite-time consensus property to achieve better synchronization. Our analysis on how communication delay and randomized chats affect learning further enables the derivation of practical variants.
  arXiv  Detail & Related papers  (2021-11-04T15:36:25Z)
• Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems [10.257042901204528]
  Cooperative Intelligent Transport System (C-ITS) is a promising network to provide safety, efficiency, sustainability, and comfortable services for automated vehicles and road infrastructures. The components of C-ITS usually generate large amounts of data, which makes it difficult to explore data science. We propose a novel Semi-a synchronous Federated Learning (SHFL) framework for C-ITS that enables elastic edge to cloud model aggregation from data sensing.
  arXiv  Detail & Related papers  (2021-10-18T07:44:34Z)
• SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems [15.796325306292134]
  Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy. Various personalized approaches have been proposed, but such approaches fail to handle underlying shifts in data distribution. This paper presents a dynamically optimized personal deep learning scheme based on blockchain and federated learning.
  arXiv  Detail & Related papers  (2021-03-12T02:57:05Z)
• Adversarial Self-Supervised Contrastive Learning [62.17538130778111]
  Existing adversarial learning approaches mostly use class labels to generate adversarial samples that lead to incorrect predictions. We propose a novel adversarial attack for unlabeled data, which makes the model confuse the instance-level identities of the perturbed data samples. We present a self-supervised contrastive learning framework to adversarially train a robust neural network without labeled data.
  arXiv  Detail & Related papers  (2020-06-13T08:24:33Z)
• Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach [61.64006416975458]
  We propose a privacy-preserving machine learning technique named Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism. It is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models.
  arXiv  Detail & Related papers  (2020-03-19T13:07:49Z)

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.