Privacy-preserving edge federated learning for intelligent mobile-health systems
Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospitals or...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
December 2024
|
| In: |
Future generation computer systems
Year: 2024, Volume: 161, Pages: 625-637 |
| ISSN: | 1872-7115 |
| DOI: | 10.1016/j.future.2024.07.035 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.future.2024.07.035 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0167739X24003972 
|
| Author Notes: | Amin Aminifar, Matin Shokri, Amir Aminifar |
MARC
| LEADER | 00000caa a2200000 c 4500 | ||
|---|---|---|---|
| 001 | 1917874979 | ||
| 003 | DE-627 | ||
| 005 | 20250716233316.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 250224s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.future.2024.07.035 |2 doi | |
| 035 | |a (DE-627)1917874979 | ||
| 035 | |a (DE-599)KXP1917874979 | ||
| 035 | |a (OCoLC)1528019542 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 084 | |a 28 |2 sdnb | ||
| 100 | 1 | |a Aminifar, Amin |e VerfasserIn |0 (DE-588)1357414544 |0 (DE-627)1917875835 |4 aut | |
| 245 | 1 | 0 | |a Privacy-preserving edge federated learning for intelligent mobile-health systems |c Amin Aminifar, Matin Shokri, Amir Aminifar |
| 264 | 1 | |c December 2024 | |
| 300 | |b Illustrationen | ||
| 300 | |a 13 | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Online verfügbar: 23. Juli 2024, Artikelversion: 7. August 2024 | ||
| 500 | |a Gesehen am 24.02.2025 | ||
| 520 | |a Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospitals or patients’ mobile devices/sensors. At the same time, transferring the data to a central location for learning is certainly not an option, due to privacy concerns and legal issues, and in certain cases, because of the communication and computation overheads. Federated Learning (FL) is the state-of-the-art collaborative ML approach for training an ML model across multiple parties holding local data samples, without sharing them. However, enabling learning from distributed data over such edge Internet of Things (IoT) systems (e.g., mobile-health and wearable technologies, involving sensitive personal/medical data) in a privacy-preserving fashion presents a major challenge mainly due to their stringent resource constraints, i.e., limited computing capacity, communication bandwidth, memory storage, and battery lifetime. In this paper, we propose a privacy-preserving edge FL framework for resource-constrained mobile-health and wearable technologies over the IoT infrastructure. We evaluate our proposed framework extensively and provide the implementation of our technique on Amazon’s AWS cloud platform based on the seizure detection application in epilepsy monitoring using wearable technologies. | ||
| 650 | 4 | |a Edge federated learning | |
| 650 | 4 | |a Mobile-health technologies | |
| 650 | 4 | |a Privacy-preserving machine learning | |
| 700 | 1 | |a Shokri, Matin |e VerfasserIn |4 aut | |
| 700 | 1 | |a Aminifar, Amir |e VerfasserIn |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Future generation computer systems |d Amsterdam [u.a.] : Elsevier Science, 1984 |g 161(2024) vom: Dez., Seite 625-637 |h Online-Ressource |w (DE-627)320604284 |w (DE-600)2020551-X |w (DE-576)094399212 |x 1872-7115 |7 nnas |a Privacy-preserving edge federated learning for intelligent mobile-health systems |
| 773 | 1 | 8 | |g volume:161 |g year:2024 |g month:12 |g pages:625-637 |g extent:13 |a Privacy-preserving edge federated learning for intelligent mobile-health systems |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.future.2024.07.035 |x Verlag |x Resolving-System |z kostenfrei |3 Volltext |
| 856 | 4 | 0 | |u https://www.sciencedirect.com/science/article/pii/S0167739X24003972 |x Verlag |z kostenfrei |3 Volltext |
| 951 | |a AR | ||
| 992 | |a 20250224 | ||
| 993 | |a Article | ||
| 994 | |a 2024 | ||
| 998 | |g 1357414544 |a Aminifar, Amin |m 1357414544:Aminifar, Amin |d 700000 |d 720000 |e 700000PA1357414544 |e 720000PA1357414544 |k 0/700000/ |k 1/700000/720000/ |p 1 |x j | ||
| 999 | |a KXP-PPN1917874979 |e 4670016032 | ||
| BIB | |a Y | ||
| SER | |a journal | ||
| JSO | |a {"title":[{"title_sort":"Privacy-preserving edge federated learning for intelligent mobile-health systems","title":"Privacy-preserving edge federated learning for intelligent mobile-health systems"}],"person":[{"family":"Aminifar","given":"Amin","display":"Aminifar, Amin","roleDisplay":"VerfasserIn","role":"aut"},{"roleDisplay":"VerfasserIn","display":"Shokri, Matin","role":"aut","family":"Shokri","given":"Matin"},{"family":"Aminifar","given":"Amir","roleDisplay":"VerfasserIn","display":"Aminifar, Amir","role":"aut"}],"type":{"bibl":"article-journal","media":"Online-Ressource"},"note":["Online verfügbar: 23. Juli 2024, Artikelversion: 7. August 2024","Gesehen am 24.02.2025"],"language":["eng"],"recId":"1917874979","origin":[{"dateIssuedDisp":"December 2024","dateIssuedKey":"2024"}],"id":{"doi":["10.1016/j.future.2024.07.035"],"eki":["1917874979"]},"name":{"displayForm":["Amin Aminifar, Matin Shokri, Amir Aminifar"]},"physDesc":[{"extent":"13 S.","noteIll":"Illustrationen"}],"relHost":[{"part":{"pages":"625-637","year":"2024","extent":"13","volume":"161","text":"161(2024) vom: Dez., Seite 625-637"},"pubHistory":["1.1984 - 29.2013; Vol. 30.2014 -"],"language":["eng"],"recId":"320604284","physDesc":[{"extent":"Online-Ressource"}],"disp":"Privacy-preserving edge federated learning for intelligent mobile-health systemsFuture generation computer systems","type":{"bibl":"periodical","media":"Online-Ressource"},"id":{"eki":["320604284"],"zdb":["2020551-X"],"issn":["1872-7115"]},"origin":[{"publisher":"Elsevier Science","dateIssuedKey":"1984","dateIssuedDisp":"1984-","publisherPlace":"Amsterdam [u.a.]"}],"title":[{"title_sort":"Future generation computer systems","title":"Future generation computer systems"}]}]} | ||
| SRT | |a AMINIFARAMPRIVACYPRE2024 | ||