mlsanityData to 14 Oct 2026

BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare

arXiv 2207.11654 ↗First posted 24 Jul 2022cs.NI cs.CR cs.LGcited by 0

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Abstract (arXiv)

This paper proposes Federated Learning (FL) based smart healthcare system where Medical Centers (MCs) train the local model using the data collected from patients and send the model weights to the miners in a blockchain-based robust framework without sharing raw data, keeping privacy preservation into deliberation. We formulate an optimization problem by maximizing the utility and minimizing the loss function considering energy consumption and FL process delay of MCs for learning effective models on distributed healthcare data underlying a blockchain-based framework. We propose a solution in two stages: first, offer a stable matching-based association algorithm to maximize the utility of both miners and MCs and then solve loss minimization using Stochastic Gradient Descent (SGD) algorithm employing FL under Differential Privacy (DP) and blockchain technology. Moreover, we incorporate blockchain technology to provide tempered resistant and decentralized model weight sharing in the proposed FL-based framework. The effectiveness of the proposed model is shown through simulation on real-world healthcare data comparing other state-of-the-art techniques.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 42 references that are in the map. First posted on arXiv 24 Jul 2022.

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References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Deep Learning with Differential Privacycited by 2,728
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  5. 2019Federated Machine Learning: Concept and Applicationscited by 412
  6. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  7. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  8. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  9. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  10. 2018Blockchained On-Device Federated Learningcited by 106
  11. 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 77
  12. 2019Collecting and Analyzing Multidimensional Data with Local Differential Privacycited by 49
5 more
  1. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  2. 2019BAFFLE : Blockchain Based Aggregator Free Federated Learningcited by 38
  3. 2020Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approachcited by 29
  4. 2019HealthFog: An Ensemble Deep Learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog Computing Environmentscited by 9
  5. 2019HealthGuard: A Machine Learning-Based Security Framework for Smart Healthcare Systemscited by 4

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