Decentralized Intelligence Network (DIN)
- URL: http://arxiv.org/abs/2407.02461v1
- Date: Tue, 2 Jul 2024 17:40:06 GMT
- Title: Decentralized Intelligence Network (DIN)
- Authors: Abraham Nash,
- Abstract summary: Decentralized Intelligence Network (DIN) addresses the challenges of data sovereignty and AI utilization caused by the fragmentation and siloing of data across providers and institutions.
This comprehensive framework overcomes access barriers to scalable data sources.
It supports effective AI training, allowing participants to maintain control over their data, benefit financially, and contribute to a decentralized, scalable ecosystem.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Decentralized Intelligence Network (DIN) addresses the significant challenges of data sovereignty and AI utilization caused by the fragmentation and siloing of data across providers and institutions. This comprehensive framework overcomes access barriers to scalable data sources previously hindered by silos by leveraging: 1) personal data stores as a prerequisite for data sovereignty; 2) a scalable federated learning protocol implemented on a public blockchain for decentralized AI training, where data remains with participants and only model parameter updates are shared; and 3) a scalable, trustless rewards mechanism to incentivize participation and ensure fair reward distribution. This framework ensures that no entity can prevent or control access to training on data offered by participants or determine financial benefits, as these processes operate on a public blockchain with an immutable record and without a third party. It supports effective AI training, allowing participants to maintain control over their data, benefit financially, and contribute to a decentralized, scalable ecosystem that leverages collective AI to develop beneficial algorithms.
Related papers
- 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) - Blockchain-enabled Trustworthy Federated Unlearning [50.01101423318312]
Federated unlearning is a promising paradigm for protecting the data ownership of distributed clients.
Existing works require central servers to retain the historical model parameters from distributed clients.
This paper proposes a new blockchain-enabled trustworthy federated unlearning framework.
arXiv Detail & Related papers (2024-01-29T07:04:48Z) - 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) - Blockchain-Based Federated Learning: Incentivizing Data Sharing and
Penalizing Dishonest Behavior [0.0]
This paper proposes a comprehensive framework that integrates data trust in federated learning with InterPlanetary File System, blockchain, and smart contracts.
The proposed model is effective in improving the accuracy of federated learning models while ensuring the security and fairness of the data-sharing process.
The research paper also presents a decentralized federated learning platform that successfully trained a CNN model on the MNIST dataset.
arXiv Detail & Related papers (2023-07-19T23:05:49Z) - Reclaiming the Digital Commons: A Public Data Trust for Training Data [2.36052383261568]
We propose that a public data trust assert control over training data for foundation models.
This trust should scrape the internet as a digital commons, to license to commercial model developers for a percentage cut of revenues from deployment.
arXiv Detail & Related papers (2023-03-16T00:12:43Z) - Mechanisms that Incentivize Data Sharing in Federated Learning [90.74337749137432]
We show how a naive scheme leads to catastrophic levels of free-riding where the benefits of data sharing are completely eroded.
We then introduce accuracy shaping based mechanisms to maximize the amount of data generated by each agent.
arXiv Detail & Related papers (2022-07-10T22:36:52Z) - APPFLChain: A Privacy Protection Distributed Artificial-Intelligence
Architecture Based on Federated Learning and Consortium Blockchain [6.054775780656853]
We propose a new system architecture called APPFLChain.
It is an integrated architecture of a Hyperledger Fabric-based blockchain and a federated-learning paradigm.
Our new system can maintain a high degree of security and privacy as users do not need to share sensitive personal information to the server.
arXiv Detail & Related papers (2022-06-26T05:30:07Z) - Federated Learning for Open Banking [42.05232310057235]
In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning.
This chapter will discuss the possible challenges for applying federated learning in the context of open banking.
arXiv Detail & Related papers (2021-08-24T14:06:16Z) - Collaborative Unsupervised Visual Representation Learning from
Decentralized Data [34.06624704343615]
We propose a novel federated unsupervised learning framework, FedU.
In this framework, each party trains models from unlabeled data independently using contrastive learning with an online network and a target network.
FedU preserves data privacy as each party only has access to its raw data.
arXiv Detail & Related papers (2021-08-14T08:34:11Z) - Trustworthy AI [75.99046162669997]
Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations.
We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
arXiv Detail & Related papers (2020-11-02T20:04:18Z)
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.