Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification

• URL: http://arxiv.org/abs/2306.10841v3
• Date: Thu, 23 Nov 2023 02:49:23 GMT
• Title: Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification
• Authors: Eunsu Goh, Dae-Yeol Kim, Kwangkee Lee, Suyeong Oh, Jong-Eui Chae, Do-Yup Kim
• Abstract summary: This paper presents a novel reference architecture for blockchain-enabled federated learning (BCFL) We define smart contract functions, stakeholders and their roles, and the use of interplanetary file system (IPFS) as key components of BCFL.
• Score: 3.1457219084519004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel reference architecture for blockchain-enabled federated learning (BCFL), a state-of-the-art approach that amalgamates the strengths of federated learning and blockchain technology.We define smart contract functions, stakeholders and their roles, and the use of interplanetary file system (IPFS) as key components of BCFL and conduct a comprehensive analysis. In traditional centralized federated learning, the selection of local nodes and the collection of learning results for each round are merged under the control of a central server. In contrast, in BCFL, all these processes are monitored and managed via smart contracts. Additionally, we propose an extension architecture to support both crossdevice and cross-silo federated learning scenarios. Furthermore, we implement and verify the architecture in a practical real-world Ethereum development environment. Our BCFL reference architecture provides significant flexibility and extensibility, accommodating the integration of various additional elements, as per specific requirements and use cases, thereby rendering it an adaptable solution for a wide range of BCFL applications. As a prominent example of extensibility, decentralized identifiers (DIDs) have been employed as an authentication method to introduce practical utilization within BCFL. This study not only bridges a crucial gap between research and practical deployment but also lays a solid foundation for future explorations in the realm of BCFL. The pivotal contribution of this study is the successful implementation and verification of a realistic BCFL reference architecture. We intend to make the source code publicly accessible shortly, fostering further advancements and adaptations within the community.

Related papers

• Forgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated Learning [8.127710748771992]
  We introduce the first VFL framework with theoretically guaranteed unlearning capabilities. Unlike prior approaches, our solution is model- and data-agnostic, offering universal compatibility. Our framework supports asynchronous unlearning, eliminating the need for all parties to be simultaneously online during the forgetting process.
  arXiv  Detail & Related papers  (2025-02-24T11:52:35Z)
• Principles and Components of Federated Learning Architectures [0.0]
  Federated Learning (FL) is a machine learning framework where multiple clients collaboratively construct a model under the orchestration of a central server. This article provides an elaborate explanation of the inherent concepts and features found within federated learning architecture.
  arXiv  Detail & Related papers  (2025-02-07T19:09:03Z)
• Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework [1.4206132527980742]
  Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy. We present the recent advances in developing APPFL, a framework and benchmarking suite for federated learning. We demonstrate the capabilities of APPFL through extensive experiments evaluating various aspects of FL, including communication efficiency, privacy preservation, computational performance, and resource utilization.
  arXiv  Detail & Related papers  (2024-09-17T22:20:26Z)
• 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)
• A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization [8.082460100928358]
  Federated learning (FL) is emerging as a sought-after distributed machine learning architecture. With advancements in network infrastructure, FL has been seamlessly integrated into edge computing. While blockchain technology promises to bolster security, practical deployment on resource-constrained edge devices remains a challenge.
  arXiv  Detail & Related papers  (2023-10-14T20:47:30Z)
• Serving Deep Learning Model in Relational Databases [70.53282490832189]
  Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains. We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks. The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
  arXiv  Detail & Related papers  (2023-10-07T06:01:35Z)
• Bayesian Federated Learning: A Survey [54.40136267717288]
  Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. The robustness and capabilities of existing FL methods are challenged by limited and dynamic data and conditions. BFL has emerged as a promising approach to address these issues.
  arXiv  Detail & Related papers  (2023-04-26T03:41:17Z)
• Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges [0.0]
  Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation. This article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators.
  arXiv  Detail & Related papers  (2022-11-15T18:51:20Z)
• FederatedScope: A Comprehensive and Flexible Federated Learning Platform via Message Passing [63.87056362712879]
  We propose a novel and comprehensive federated learning platform, named FederatedScope, which is based on a message-oriented framework. Compared to the procedural framework, the proposed message-oriented framework is more flexible to express heterogeneous message exchange. We conduct a series of experiments on the provided easy-to-use and comprehensive FL benchmarks to validate the correctness and efficiency of FederatedScope.
  arXiv  Detail & Related papers  (2022-04-11T11:24:21Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)
• Wireless Communications for Collaborative Federated Learning [160.82696473996566]
  Internet of Things (IoT) devices may not be able to transmit their collected data to a central controller for training machine learning models. Google's seminal FL algorithm requires all devices to be directly connected with a central controller. This paper introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller.
  arXiv  Detail & Related papers  (2020-06-03T20:00:02Z)
• A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus [20.787163387487816]
  In mobile computing scenarios, federated learning protects users from exposing their private data, while cooperatively training the global model for a variety of real-world applications. Security of federated learning is increasingly being questioned, due to the malicious clients or central servers' constant attack to the global model or user privacy data. We propose a decentralized federated learning framework based on blockchain, i.e., a Committee consensus (BFLC) framework.
  arXiv  Detail & Related papers  (2020-04-02T02:04:16Z)

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.