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Poster: A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain

Published: 06 November 2019 Publication History

Abstract

Federated learning (FL) is promising in supporting collaborative learning applications that involve large datasets, massively distributed data owners and unreliable network connectivity. To protect data privacy, existing FL approaches adopt (k,n)-threshold secret sharing schemes, based on the semi-honest assumption for clients, to enable secure multiparty computation in local model update exchange which deals with random client dropouts at the cost of increasing data size. These approaches adopt the semi-honest assumption for clients, therefore they are vulnerable to malicious clients. In this work, we propose a blockchain-based privacy-preserving federated learning (BC-based PPFL) framework, which leverages the immutability and decentralized trust properties of blockchain to provide provenance of model updates. Our proof-of-concept implementation of BC-based PPFL demonstrates it is practical for secure aggregation of local model updates in the federated setting.

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Juan Benet. Protocol Labs, 2016. InterPlanetary File System (IPFS), 2016. https://www.ipfs.io Retrieved July 07, 2019 from
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Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander. 2019. Towards Federated Learning at Scale: System Design. In Proceedings of The 2nd SysML Conference, Stanford, California, March, 2019.
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Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical Secure Aggregation for Privacy-Preserving Machine Learning. In Proceedings of ACM Conference on Computer and Communications Security, 2017.
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Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim. 2019. On-Device Federated Learning via Blockchain and its Latency Analysis. In IEEE Communications Letters, 2019.
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Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proccedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017.
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Cited By

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  • (2025)Blockchain-Based Privacy-Enhancing Federated Learning in Smart Healthcare: A SurveyBlockchains10.3390/blockchains30100013:1(1)Online publication date: 1-Jan-2025
  • (2025)Reinforcement Learning-Based Personalized Differentially Private Federated LearningIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351581420(465-477)Online publication date: 2025
  • (2025)Securing the collective intelligence: a comprehensive review of federated learning security attacks and defensive strategiesKnowledge and Information Systems10.1007/s10115-025-02339-zOnline publication date: 21-Jan-2025
  • Show More Cited By

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Published In

cover image ACM Conferences
CCS '19: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security
November 2019
2755 pages
ISBN:9781450367479
DOI:10.1145/3319535
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2019

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Author Tags

  1. blockchain
  2. federated learning
  3. privacy

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  • Poster

Funding Sources

  • NSA SoS Initiative
  • Ripple University Blockchain Research Initiative
  • NSF DGE

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CCS '19
Sponsor:

Acceptance Rates

CCS '19 Paper Acceptance Rate 149 of 934 submissions, 16%;
Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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CCS '25

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Cited By

View all
  • (2025)Blockchain-Based Privacy-Enhancing Federated Learning in Smart Healthcare: A SurveyBlockchains10.3390/blockchains30100013:1(1)Online publication date: 1-Jan-2025
  • (2025)Reinforcement Learning-Based Personalized Differentially Private Federated LearningIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351581420(465-477)Online publication date: 2025
  • (2025)Securing the collective intelligence: a comprehensive review of federated learning security attacks and defensive strategiesKnowledge and Information Systems10.1007/s10115-025-02339-zOnline publication date: 21-Jan-2025
  • (2024)Next-Generation Technologies for Secure Future Communication-based Social-Media 3.0 and Smart EnvironmentIECE Transactions on Sensing, Communication, and Control10.62762/TSCC.2024.3228981:2(101-125)Online publication date: 27-Nov-2024
  • (2024)Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa NetworksSensors10.3390/s2422733624:22(7336)Online publication date: 17-Nov-2024
  • (2024)Research on Privacy Protection in Federated Learning Combining Distillation Defense and BlockchainElectronics10.3390/electronics1304067913:4(679)Online publication date: 6-Feb-2024
  • (2024)Privacy preservation using optimized Federated Learning: A critical surveyIntelligent Decision Technologies10.3233/IDT-23010418:1(135-149)Online publication date: 20-Feb-2024
  • (2024)A Study of Federated Learning with Internet of Things for Data Privacy and Security using Privacy Preserving TechniquesRecent Patents on Engineering10.2174/187221211766623011211025718:1Online publication date: Jan-2024
  • (2024)Federated Medical Learning Framework Based on Blockchain and Homomorphic EncryptionWireless Communications & Mobile Computing10.1155/2024/81386442024Online publication date: 1-Jan-2024
  • (2024)Blockchained Federated Learning for Internet of Things: A Comprehensive SurveyACM Computing Surveys10.1145/365909956:10(1-37)Online publication date: 22-Jun-2024
  • Show More Cited By

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