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Responsible Recommendation Services with Blockchain Empowered Asynchronous Federated Learning

Published: 27 July 2024 Publication History

Abstract

Privacy and trust are highly demanding in practical recommendation engines. Although Federated Learning (FL) has significantly addressed privacy concerns, commercial operators are still worried about several technical challenges while bringing FL into production. In addition, classical FL has several intrinsic operational limitations such as single-point failure, data and model tampering, and heterogenic clients participating in the FL process. To address these challenges in practical recommenders, we propose a responsible recommendation generation framework based on blockchain-empowered asynchronous FL that can be adopted for any model-based recommender system. In standard FL settings, we build an additional aggregation layer in which multiple trusted nodes guided by a mediator component perform gradient aggregation to achieve an optimal model locally in a parallel fashion. The mediator partitions users into K clusters, and each cluster is represented by a cluster head. Once a cluster gets semi-global convergence, the cluster head transmits model gradients to the FL server for global aggregation. In addition the trusted cluster heads are responsible to submit the converged semi-global model to a blockchain to ensure tamper resilience. In our settings, an additional mediator component works like an independent observer that monitors the performance of each cluster head, updates a reward score, and records it into a digital ledger. Finally, evaluation results on three diversified benchmarks illustrate that the recommendation performance on selected measures is considerably comparable with the standard and federated version of a well-known neural collaborative filtering recommender.

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Cited By

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  • (2025)Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and BlockchainACM Computing Surveys10.1145/370898257:5(1-35)Online publication date: 9-Jan-2025
  • (2024) ASMCC + : A Secure Authentication Scheme for Mobile Cloud Computing Environment Based on Zero Trust Architecture IEEE Transactions on Consumer Electronics10.1109/TCE.2024.341543770:3(6236-6249)Online publication date: Aug-2024

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  1. Responsible Recommendation Services with Blockchain Empowered Asynchronous Federated Learning

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    Published In

    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 4
    August 2024
    563 pages
    EISSN:2157-6912
    DOI:10.1145/3613644
    • Editor:
    • Huan Liu
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 July 2024
    Online AM: 23 November 2023
    Accepted: 04 November 2023
    Revised: 05 September 2023
    Received: 11 May 2023
    Published in TIST Volume 15, Issue 4

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    Author Tags

    1. Responsible recommender systems
    2. privacy protection
    3. federated learning
    4. blockchain

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    • National Natural Science Foundation of China
    • Sichuan Science and Technology Program

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    • (2025)Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and BlockchainACM Computing Surveys10.1145/370898257:5(1-35)Online publication date: 9-Jan-2025
    • (2024) ASMCC + : A Secure Authentication Scheme for Mobile Cloud Computing Environment Based on Zero Trust Architecture IEEE Transactions on Consumer Electronics10.1109/TCE.2024.341543770:3(6236-6249)Online publication date: Aug-2024

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