BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare
- URL: http://arxiv.org/abs/2207.11654v2
- Date: Wed, 27 Jul 2022 08:43:44 GMT
- Title: BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare
- Authors: Moirangthem Biken Singh, and Ajay Pratap
- Abstract summary: 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.
- Score: 0.3867363075280543
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- 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) - Client Orchestration and Cost-Efficient Joint Optimization for
NOMA-Enabled Hierarchical Federated Learning [55.49099125128281]
We propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation.
We show that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.
arXiv Detail & Related papers (2023-11-03T13:34:44Z) - The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies [2.6391879803618115]
We study the practical implications of outsourcing the orchestration of federated learning to a democratic setting such as in a blockchain.
Using simulation, we evaluate the blockchained FL operation by applying two different ML models on the well-known MNIST and CIFAR-10 datasets.
Our results show the high impact of model inconsistencies on the accuracy of the models (up to a 35% decrease in prediction accuracy)
arXiv Detail & Related papers (2023-10-11T13:18:23Z) - Blockchain-empowered Federated Learning for Healthcare Metaverses:
User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses.
We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
arXiv Detail & Related papers (2023-07-29T12:54:03Z) - Blockchain-based Monitoring for Poison Attack Detection in Decentralized
Federated Learning [2.322461721824713]
Federated Learning (FL) is a machine learning technique that addresses the privacy challenges in terms of access rights of local datasets.
In decentralized FL, the chief is eliminated from the learning process as workers collaborate between each other to train the global model.
We propose a technique which consists in decoupling the monitoring phase from the detection phase in defenses against poisoning attacks.
arXiv Detail & Related papers (2022-09-30T19:07:29Z) - Latency Optimization for Blockchain-Empowered Federated Learning in
Multi-Server Edge Computing [24.505675843652448]
In this paper, we study a new latency optimization problem for federated learning (BFL) in multi-server edge computing.
In this system model, distributed mobile devices (MDs) communicate with a set of edge servers (ESs) to handle both machine learning (ML) model training and block mining simultaneously.
arXiv Detail & Related papers (2022-03-18T00:38:29Z) - Towards On-Device Federated Learning: A Direct Acyclic Graph-based
Blockchain Approach [2.9202274421296943]
This paper introduces a framework for empowering Federated Learning using Direct Acyclic Graph (DAG)-based blockchain systematically (DAG-FL)
Two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on different nodes to elaborate the operation of DAG-FL consensus mechanism.
arXiv Detail & Related papers (2021-04-27T10:29:38Z) - Blockchain Assisted Decentralized Federated Learning (BLADE-FL):
Performance Analysis and Resource Allocation [119.19061102064497]
We propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL)
In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, competes to generate a block based on the received models, and then aggregates the models from the generated block before its local training of the next round.
We explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
arXiv Detail & Related papers (2021-01-18T07:19:08Z) - Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with
Lazy Clients [124.48732110742623]
We propose a novel framework by integrating blockchain into Federated Learning (FL)
BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning.
It gives rise to a new problem of training deficiency, caused by lazy clients who plagiarize others' trained models and add artificial noises to conceal their cheating behaviors.
arXiv Detail & Related papers (2020-12-02T12:18:27Z) - Resource Management for Blockchain-enabled Federated Learning: A Deep
Reinforcement Learning Approach [54.29213445674221]
Federated Learning (BFL) enables mobile devices to collaboratively train neural network models required by a Machine Learning Model Owner (MLMO)
The issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency.
We propose to use the Deep Reinforcement Learning (DRL) to derive the optimal decisions for theO.
arXiv Detail & Related papers (2020-04-08T16:29:19Z)
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.