Blockchained Federated Learning for Privacy and Security Preservation : Practical Example of Diagnosing Cerebellar Ataxia
Cerebellar ataxia (CA) refers to the incoordination of movements of the eyes, speech, trunk, and limbs caused by cerebellar dysfunction. Conventional machine learning (ML) utilizes centralised databases to train a model of diagnosing CA. Despite the high accuracy, these approaches raise privacy concern as participants' data revealed in the data centre. Federated learning is an effective distributed solution to exchange only the ML model weight rather than the raw data. However, FL is also vulnerable to network attacks from malicious devices. In this study, we depict the concept of blockchained FL with individual's validators. We simulate the proposed approach with real-world dataset collected from kinematic sensors of CA individuals with four geographically separated clinics. Experimental results show the blockchained FL maintains competitive accuracy of 89.30%, while preserving both privacy and security.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2022 |
|---|---|
| Enthalten in: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference - 2022(2022) vom: 09. Juli, Seite 4925-4928 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Ngo, Thang [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 13.09.2022 Date Revised 21.10.2022 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1109/EMBC48229.2022.9871371 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM346031729 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM346031729 | ||
| 003 | DE-627 | ||
| 005 | 20231226030448.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1109/EMBC48229.2022.9871371 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1153.xml |
| 035 | |a (DE-627)NLM346031729 | ||
| 035 | |a (NLM)36086180 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Ngo, Thang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Blockchained Federated Learning for Privacy and Security Preservation |b Practical Example of Diagnosing Cerebellar Ataxia |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 13.09.2022 | ||
| 500 | |a Date Revised 21.10.2022 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Cerebellar ataxia (CA) refers to the incoordination of movements of the eyes, speech, trunk, and limbs caused by cerebellar dysfunction. Conventional machine learning (ML) utilizes centralised databases to train a model of diagnosing CA. Despite the high accuracy, these approaches raise privacy concern as participants' data revealed in the data centre. Federated learning is an effective distributed solution to exchange only the ML model weight rather than the raw data. However, FL is also vulnerable to network attacks from malicious devices. In this study, we depict the concept of blockchained FL with individual's validators. We simulate the proposed approach with real-world dataset collected from kinematic sensors of CA individuals with four geographically separated clinics. Experimental results show the blockchained FL maintains competitive accuracy of 89.30%, while preserving both privacy and security | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Nguyen, Dinh C |e verfasserin |4 aut | |
| 700 | 1 | |a Pathirana, Pubudu N |e verfasserin |4 aut | |
| 700 | 1 | |a Corben, Louise A |e verfasserin |4 aut | |
| 700 | 1 | |a Horne, Malcolm |e verfasserin |4 aut | |
| 700 | 1 | |a Szmulewicz, David J |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference |d 2007 |g 2022(2022) vom: 09. Juli, Seite 4925-4928 |w (DE-627)NLM175007292 |x 2694-0604 |7 nnns |
| 773 | 1 | 8 | |g volume:2022 |g year:2022 |g day:09 |g month:07 |g pages:4925-4928 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1109/EMBC48229.2022.9871371 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 2022 |j 2022 |b 09 |c 07 |h 4925-4928 | ||