A Blockchain Solution for Collaborative Machine Learning over IoT
- URL: http://arxiv.org/abs/2311.14136v1
- Date: Thu, 23 Nov 2023 18:06:05 GMT
- Title: A Blockchain Solution for Collaborative Machine Learning over IoT
- Authors: Carlos Beis-Penedo and Francisco Troncoso-Pastoriza and Rebeca P.
D\'iaz-Redondo and Ana Fern\'andez-Vilas and Manuel Fern\'andez-Veiga and
Mart\'in Gonz\'alez Soto
- Abstract summary: Federated learning (FL) and blockchain technologies have emerged as promising approaches to address these challenges.
We present a novel IoT solution that combines the incremental learning vector quantization algorithm (XuILVQ) with blockchain technology.
Our proposed architecture addresses the shortcomings of existing blockchain-based FL solutions by reducing computational and communication overheads while maintaining data privacy and security.
- Score: 0.31410859223862103
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rapid growth of Internet of Things (IoT) devices and applications has led
to an increased demand for advanced analytics and machine learning techniques
capable of handling the challenges associated with data privacy, security, and
scalability. Federated learning (FL) and blockchain technologies have emerged
as promising approaches to address these challenges by enabling decentralized,
secure, and privacy-preserving model training on distributed data sources. In
this paper, we present a novel IoT solution that combines the incremental
learning vector quantization algorithm (XuILVQ) with Ethereum blockchain
technology to facilitate secure and efficient data sharing, model training, and
prototype storage in a distributed environment. Our proposed architecture
addresses the shortcomings of existing blockchain-based FL solutions by
reducing computational and communication overheads while maintaining data
privacy and security. We assess the performance of our system through a series
of experiments, showcasing its potential to enhance the accuracy and efficiency
of machine learning tasks in IoT settings.
Related papers
- Blockchain Integrated Federated Learning in Edge-Fog-Cloud Systems for IoT based Healthcare Applications A Survey [18.36339203254509]
Federated learning, a new distributed paradigm, supports collaborative learning while preserving privacy.
The integration of federated learning and blockchain is particularly advantageous for handling sensitive data, such as in healthcare.
This survey article explores the architecture, structure, functions, and characteristics of federated learning and blockchain, their applications in various computing paradigms, and evaluates their implementations in healthcare.
arXiv Detail & Related papers (2024-06-08T16:36:48Z) - 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) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - Generative AI-enabled Blockchain Networks: Fundamentals, Applications,
and Case Study [73.87110604150315]
Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology.
In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
arXiv Detail & Related papers (2024-01-28T10:46:17Z) - 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) - Federated Learning-Empowered AI-Generated Content in Wireless Networks [58.48381827268331]
Federated learning (FL) can be leveraged to improve learning efficiency and achieve privacy protection for AIGC.
We present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content.
arXiv Detail & Related papers (2023-07-14T04:13:11Z) - A Survey on Blockchain-Based Federated Learning and Data Privacy [1.0499611180329802]
Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the models transmission.
On the other hand, federated learning has the drawback of data leakage due to the lack of privacy-preserving mechanisms employed during storage, transfer, and sharing.
This survey aims to compare the performance and security of various data privacy mechanisms adopted in blockchain-based federated learning architectures.
arXiv Detail & Related papers (2023-06-29T23:43:25Z) - Blockchained Federated Learning for Internet of Things: A Comprehensive
Survey [30.032413027090275]
This survey comprehensively reviewsed Federated Learning (BlockFL)
We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios.
Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training.
arXiv Detail & Related papers (2023-05-08T07:14:50Z) - A Reliable Data-transmission Mechanism using Blockchain in Edge
Computing Scenarios [22.92724948442006]
We propose a data transmission mechanism based on blockchain, which uses the distributed architecture of blockchain to ensure that the data is not tampered with.
In the end, the simulation results show that the proposed scheme can ensure the reliability of data transmission in the Internet of things to a great extent.
arXiv Detail & Related papers (2022-02-07T00:49:41Z) - Resource Management for Blockchain-enabled Federated Learning: A Deep
Reinforcement Learning Approach [54.29213445674221]
Federated Learning (BFL) enables mobile devices to collaboratively train neural network models required by a Machine Learning Model Owner (MLMO)
The issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency.
We propose to use the Deep Reinforcement Learning (DRL) to derive the optimal decisions for theO.
arXiv Detail & Related papers (2020-04-08T16:29:19Z)
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.