Blockchain-Based Federated Learning in Mobile Edge Networks with
Application in Internet of Vehicles
- URL: http://arxiv.org/abs/2103.01116v1
- Date: Mon, 1 Mar 2021 16:38:40 GMT
- Title: Blockchain-Based Federated Learning in Mobile Edge Networks with
Application in Internet of Vehicles
- Authors: Rui Wang, Heju Li, Erwu Liu
- Abstract summary: Privacy concerns are major bottlenecks for data providers to share private data in traditional IoV networks.
In this paper, we propose an autonomous blockchain empowered privacy-preserving FL framework, where the mobile edge computing (MEC) technology was naturally integrated in IoV system.
- Score: 7.038557568936009
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rapid increase of the data scale in Internet of Vehicles (IoV) system
paradigm, hews out new possibilities in boosting the service quality for the
emerging applications through data sharing. Nevertheless, privacy concerns are
major bottlenecks for data providers to share private data in traditional IoV
networks. To this end, federated learning (FL) as an emerging learning
paradigm, where data providers only send local model updates trained on their
local raw data rather than upload any raw data, has been recently proposed to
build a privacy-preserving data sharing models. Unfortunately, by analyzing on
the differences of uploaded local model updates from data providers, private
information can still be divulged, and performance of the system cannot be
guaranteed when partial federated nodes executes malicious behavior.
Additionally, traditional cloud-based FL poses challenges to the communication
overhead with the rapid increase of terminal equipment in IoV system. All these
issues inspire us to propose an autonomous blockchain empowered
privacy-preserving FL framework in this paper, where the mobile edge computing
(MEC) technology was naturally integrated in IoV system.
Related papers
- Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems [11.544642210389894]
Federated recommender systems have been enhanced through data sharing and continuous model updates.
Given the sensitivity of IoT data, transparent data processing in data sharing and model updates is paramount.
Existing methods fall short in tracing the flow of shared data and the evolution of model updates.
We present LIBERATE, a privacy-traceable federated recommender system.
arXiv Detail & Related papers (2024-06-07T07:21:21Z) - Swarm Learning: A Survey of Concepts, Applications, and Trends [3.55026004901472]
Deep learning models have raised privacy and security concerns due to their reliance on large datasets on central servers.
Federated learning (FL) has introduced a novel approach to building a versatile, large-scale machine learning framework.
Swarm learning (SL) has been proposed in collaboration with Hewlett Packard Enterprise (HPE)
SL represents a decentralized machine learning framework that leverages blockchain technology for secure, scalable, and private data management.
arXiv Detail & Related papers (2024-05-01T14:59:24Z) - Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand [57.82021900505197]
Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications.
Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs.
We propose a decentralized data-sharing incentive mechanism based on multi-intelligent reinforcement learning to learn the supply-demand balance in markets.
arXiv Detail & Related papers (2024-03-29T14:58:28Z) - Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks [55.340315838742015]
Negligence on the security of infotainment data communication in IoV networks can unintentionally open an easy access point for social engineering attacks.
In particular, we first classify data communication in the IoV network, examine the security focus of each data communication, and then develop a differentiated security architecture to provide security protection on a file-to-file basis.
arXiv Detail & Related papers (2024-03-29T12:01:31Z) - 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) - Personalized Federated Learning with Attention-based Client Selection [57.71009302168411]
We propose FedACS, a new PFL algorithm with an Attention-based Client Selection mechanism.
FedACS integrates an attention mechanism to enhance collaboration among clients with similar data distributions.
Experiments on CIFAR10 and FMNIST validate FedACS's superiority.
arXiv Detail & Related papers (2023-12-23T03:31:46Z) - A Quality-of-Service Compliance System using Federated Learning and
Optimistic Rollups [0.0]
A parallel trend is the rise of phones and tablets as primary computing devices for many people.
The powerful sensors present on these devices combined with the fact that they are mobile, mean they have access to data of an unprecedentedly diverse and private nature.
Models learned on such data hold the promise of greatly improving usability by powering more intelligent applications, but the sensitive nature of the data means there are risks and responsibilities to storing it in a centralized location.
We propose the use of Federated Learning (FL) so that specific data about services performed by clients do not leave the source machines.
arXiv Detail & Related papers (2023-11-14T20:02:37Z) - Privacy-preserving design of graph neural networks with applications to
vertical federated learning [56.74455367682945]
We present an end-to-end graph representation learning framework called VESPER.
VESPER is capable of training high-performance GNN models over both sparse and dense graphs under reasonable privacy budgets.
arXiv Detail & Related papers (2023-10-31T15:34:59Z) - PS-FedGAN: An Efficient Federated Learning Framework Based on Partially
Shared Generative Adversarial Networks For Data Privacy [56.347786940414935]
Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation.
This work proposes a novel FL framework that requires only partial GAN model sharing.
Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions.
arXiv Detail & Related papers (2023-05-19T05:39:40Z) - Federated Learning for Internet of Things: Applications, Challenges, and
Opportunities [20.935789038643936]
Federated Learning (FL) is an act of collaboration between multiple clients without requiring the data to be brought to a central point.
We discuss the opportunities and challenges of FL for IoT platforms, as well as how it can enable future IoT applications.
arXiv Detail & Related papers (2021-11-15T02:06:12Z) - 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)
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.