BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse

• URL: http://arxiv.org/abs/2410.21675v1
• Date: Tue, 29 Oct 2024 02:52:49 GMT
• Title: BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse
• Authors: Wenbo Liu, Handi Chen, Edith C. H. Ngai,
• Abstract summary: We propose BF-Meta, a secure blockchain-empowered FL framework with decentralized model aggregation. In this paper, we propose BF-Meta to mitigate the negative influence of malicious users and provide secure virtual services in the metaverse.
• Score: 3.98794322831072
• License:
• Abstract: The metaverse, emerging as a revolutionary platform for social and economic activities, provides various virtual services while posing security and privacy challenges. Wearable devices serve as bridges between the real world and the metaverse. To provide intelligent services without revealing users' privacy in the metaverse, leveraging federated learning (FL) to train models on local wearable devices is a promising solution. However, centralized model aggregation in traditional FL may suffer from external attacks, resulting in a single point of failure. Furthermore, the absence of incentive mechanisms may weaken users' participation during FL training, leading to degraded performance of the trained model and reduced quality of intelligent services. In this paper, we propose BF-Meta, a secure blockchain-empowered FL framework with decentralized model aggregation, to mitigate the negative influence of malicious users and provide secure virtual services in the metaverse. In addition, we design an incentive mechanism to give feedback to users based on their behaviors. Experiments conducted on five datasets demonstrate the effectiveness and applicability of BF-Meta.

Related papers

• 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)
• SaFL: Sybil-aware Federated Learning with Application to Face Recognition [13.914187113334222]
  Federated Learning (FL) is a machine learning paradigm to conduct collaborative learning among clients on a joint model. On the downside, FL raises security and privacy concerns that have just started to be studied. This paper proposes a new defense method against poisoning attacks in FL called SaFL.
  arXiv  Detail & Related papers  (2023-11-07T21:06:06Z)
• An Adaptive and Modular Blockchain Enabled Architecture for a Decentralized Metaverse [22.632151691692776]
  We propose an adaptive and modular blockchain-enabled architecture for a decentralized metaverse. The solution includes an adaptive consensus/ledger protocol based on a modular blockchain. In addition, we propose the concept of Non-Fungible Resource (NFR) to virtualize idle resources.
  arXiv  Detail & Related papers  (2023-09-07T06:21:06Z)
• Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
  We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
  arXiv  Detail & Related papers  (2023-07-29T12:54:03Z)
• Defending Against Poisoning Attacks in Federated Learning with Blockchain [12.840821573271999]
  We propose a secure and reliable federated learning system based on blockchain and distributed ledger technology. Our system incorporates a peer-to-peer voting mechanism and a reward-and-slash mechanism, which are powered by on-chain smart contracts, to detect and deter malicious behaviors.
  arXiv  Detail & Related papers  (2023-07-02T11:23:33Z)
• Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computing [21.26290266786857]
  We employ Federated Learning (FL) and prominent features of blockchain into Mobile Edge Computing architecture. FL is advantageous for mobile devices with constrained connectivity since it requires model updates to be delivered to a central point. We propose a fully asynchronoused Federated Learning framework referred to as BFL-MEC, in which the mobile clients evolve independently yet guarantee stability in the global learning process.
  arXiv  Detail & Related papers  (2023-02-26T08:08:23Z)
• Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning [98.05061014090913]
  Federated learning (FL) emerges as a popular distributed learning schema that learns from a set of participating users without requiring raw data to be shared. adversarial training (AT) provides a sound solution for centralized learning, extending its usage for FL users has imposed significant challenges. We show that existing FL techniques cannot effectively propagate adversarial robustness among non-iid users. We propose a simple yet effective propagation approach that transfers robustness through carefully designed batch-normalization statistics.
  arXiv  Detail & Related papers  (2021-06-18T15:52:33Z)
• Meta Federated Learning [57.52103907134841]
  Federated Learning (FL) is vulnerable to training time adversarial attacks. We propose Meta Federated Learning ( Meta-FL) which not only is compatible with secure aggregation protocol but also facilitates defense against backdoor attacks.
  arXiv  Detail & Related papers  (2021-02-10T16:48:32Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients [124.48732110742623]
  We propose a novel framework by integrating blockchain into Federated Learning (FL) BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning. It gives rise to a new problem of training deficiency, caused by lazy clients who plagiarize others' trained models and add artificial noises to conceal their cheating behaviors.
  arXiv  Detail & Related papers  (2020-12-02T12:18:27Z)
• A Secure Federated Learning Framework for 5G Networks [44.40119258491145]
  Federated Learning (FL) has been proposed as an emerging paradigm to build machine learning models using distributed training datasets. There are two critical security threats: poisoning and membership inference attacks. We propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from involving in FL.
  arXiv  Detail & Related papers  (2020-05-12T13:27:23Z)
• 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.