Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality

• URL: http://arxiv.org/abs/2310.19287v1
• Date: Mon, 30 Oct 2023 06:05:50 GMT
• Title: Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality
• Authors: Ajay Kumar Shrestha, Faijan Ahamad Khan, Mohammed Afaan Shaikh, Amir Jaberzadeh and Jason Geng
• Abstract summary: The paper focuses on enhancing the trustworthiness of participating nodes through a trust penalization mechanism. The proposed system aims to create a fair, secure and transparent environment for collaborative machine learning without compromising data privacy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paper presents an innovative approach to address the challenges of scalability and reliability in Distributed Federated Learning by leveraging the integration of blockchain technology. The paper focuses on enhancing the trustworthiness of participating nodes through a trust penalization mechanism while also enabling asynchronous functionality for efficient and robust model updates. By combining Semi-Decentralized Federated Learning with Blockchain (SDFL-B), the proposed system aims to create a fair, secure and transparent environment for collaborative machine learning without compromising data privacy. The research presents a comprehensive system architecture, methodologies, experimental results, and discussions that demonstrate the advantages of this novel approach in fostering scalable and reliable SDFL-B systems.

Related papers

• Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
  In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions. This paper proposes a digital twin (DT) and federated digital twin (FL) scheme. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
  arXiv  Detail & Related papers  (2024-11-04T17:48:02Z)
• Voltran: Unlocking Trust and Confidentiality in Decentralized Federated Learning Aggregation [12.446757264387564]
  We present Voltran, an innovative hybrid platform designed to achieve trust, confidentiality, and robustness for Federated Learning (FL) We offload the FL aggregation into TEE to provide an isolated, trusted and customizable off-chain execution. We provide strong scalability on multiple FL scenarios by introducing a multi-SGX parallel execution strategy.
  arXiv  Detail & Related papers  (2024-08-13T13:33:35Z)
• Trust and Resilience in Federated Learning Through Smart Contracts Enabled Decentralized Systems [1.9874264019909984]
  We present a study of a Federated Learning (FL) system, based on the use of decentralized architectures to ensure trust and increase reliability. The system is based on the idea that the FL collaborators upload the (ciphered) model parameters on the Inter-Planetary File System (IPFS) and interact with a dedicated smart contract to track their behavior.
  arXiv  Detail & Related papers  (2024-07-09T13:50:32Z)
• When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain [10.099134773737939]
  Machine learning models offer the capability to forecast future energy production or consumption. However, legal and policy constraints within specific energy sectors present technical hurdles in utilizing data from diverse sources. We propose adopting a Swarm Learning scheme, which replaces the centralized server with a blockchain-based distributed network.
  arXiv  Detail & Related papers  (2024-06-07T08:42:26Z)
• Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach [20.74679353443655]
  We introduce a framework that melds blockchain with federated learning, thereby ensuring an immutable record of unlearning requests and actions. Our key contributions encompass a certification mechanism for the unlearning process, the enhancement of data security and privacy, and the optimization of data management.
  arXiv  Detail & Related papers  (2024-05-27T04:35:49Z)
• 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)
• Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0 [59.94605620983965]
  We design a Graph Attention Network (GAT)-based reliable block propagation optimization framework for blockchain-enabled Web 3.0. To achieve the reliability of block propagation, we introduce a reputation mechanism based on the subjective logic model. Considering that the GAT possesses the excellent ability to process graph-structured data, we utilize the GAT with reinforcement learning to obtain the optimal block propagation trajectory.
  arXiv  Detail & Related papers  (2024-03-20T01:58:38Z)
• Reliable Federated Disentangling Network for Non-IID Domain Feature [62.73267904147804]
  In this paper, we propose a novel reliable federated disentangling network, termed RFedDis. To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling. Our proposed RFedDis provides outstanding performance with a high degree of reliability as compared to other state-of-the-art FL approaches.
  arXiv  Detail & Related papers  (2023-01-30T11:46:34Z)
• Towards a Secure and Reliable Federated Learning using Blockchain [5.910619900053764]
  Federated learning (FL) is a distributed machine learning technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. Despite advantages, FL still suffers from several challenges related to reliability, tractability, and anonymity. We propose a secure and trustworthy blockchain framework (SRB-FL) tailored to FL.
  arXiv  Detail & Related papers  (2022-01-27T04:09:53Z)
• 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)
• RoFL: Attestable Robustness for Secure Federated Learning [59.63865074749391]
  Federated Learning allows a large number of clients to train a joint model without the need to share their private data. To ensure the confidentiality of the client updates, Federated Learning systems employ secure aggregation. We present RoFL, a secure Federated Learning system that improves robustness against malicious clients.
  arXiv  Detail & Related papers  (2021-07-07T15:42:49Z)

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.