Accelerating Federated Learning via Modified Local Model Update Based on Individual Performance Metric
The privacy-preserving federated learning (FL) algorithm is considered one of the most widely used distributed training algorithms. Its effectiveness is primarily observed when the datasets on the clients are independent, identically distributed (IID), and balanced. However, in real-world situations...
Uloženo v:
| Vydáno v: | 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) s. 1 - 6 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
19.07.2023
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | The privacy-preserving federated learning (FL) algorithm is considered one of the most widely used distributed training algorithms. Its effectiveness is primarily observed when the datasets on the clients are independent, identically distributed (IID), and balanced. However, in real-world situations, the datasets on the clients are often non-IID, leading to varied data and feature distributions, thereby adversely affecting the performance of federated learning. To address this challenge, this work applies a modified local model update mechanism based on the individual performance metric of all clients' models under training calculated on a reference testing dataset that resides on the server side. The proposed modification enhances each client model's performance individually, regardless of its data distribution. For instance, if a client model poorly predicts certain classes, it will receive a higher percentage and weighting factor from other models that perform better in predicting those classes. The empirical studies of the modified algorithm for FedAvg and FedProx aggregation methods under IId and non-IID data distribution with MNIST and CIFAR10 datasets show that this approach can speed up the training process, increase the overall system accuracy, and reduce the number of communication rounds. |
|---|---|
| AbstractList | The privacy-preserving federated learning (FL) algorithm is considered one of the most widely used distributed training algorithms. Its effectiveness is primarily observed when the datasets on the clients are independent, identically distributed (IID), and balanced. However, in real-world situations, the datasets on the clients are often non-IID, leading to varied data and feature distributions, thereby adversely affecting the performance of federated learning. To address this challenge, this work applies a modified local model update mechanism based on the individual performance metric of all clients' models under training calculated on a reference testing dataset that resides on the server side. The proposed modification enhances each client model's performance individually, regardless of its data distribution. For instance, if a client model poorly predicts certain classes, it will receive a higher percentage and weighting factor from other models that perform better in predicting those classes. The empirical studies of the modified algorithm for FedAvg and FedProx aggregation methods under IId and non-IID data distribution with MNIST and CIFAR10 datasets show that this approach can speed up the training process, increase the overall system accuracy, and reduce the number of communication rounds. |
| Author | Barhoush, Mahdi Schmeink, Anke Ayad, Ahmad |
| Author_xml | – sequence: 1 givenname: Mahdi surname: Barhoush fullname: Barhoush, Mahdi email: mahdi.barhoush@inda.rwth-aachen.de organization: RWTH University,Chair of Information Theory and Data Analytics (INDA),Aachen,Germany – sequence: 2 givenname: Ahmad surname: Ayad fullname: Ayad, Ahmad email: ahmad.ayad@inda.rwth-aachen.de organization: RWTH University,Chair of Information Theory and Data Analytics (INDA),Aachen,Germany – sequence: 3 givenname: Anke surname: Schmeink fullname: Schmeink, Anke email: schmeink@inda.rwth-aachen.de organization: RWTH University,Chair of Information Theory and Data Analytics (INDA),Aachen,Germany |
| BookMark | eNo1T01LwzAYjqAHnfsHHoL31jdJuzTHWTottOjBnUf65q0EunRkdeC_t0U9PZ888Nyx6zAGYuxRQCoEmKe6rMqyrXJdKEglSJUKkLkCDVdsbbQpVA5KSqPlLeu3iDRQtJMPn3xHbqHkeEM2hsW6eMvb0fneL-6IdlgkDXx_cnOTP9vzHIyB18H5i3dfc-GdYj_Gow1IvKUperxnN70dzrT-wxXb76qP8jVp3l7qctskXggzJZh1ZKzbCMwcAuQbFEIWpHslM00CO6mlI9BKImG-BB3ZTBjtsMgJnFqxh99dT0SHU_RHG78P__fVD6UaVrg |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICECCME57830.2023.10253070 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Xplore Electronic Library IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9798350322972 |
| EndPage | 6 |
| ExternalDocumentID | 10253070 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Ministry of Education funderid: 10.13039/100010002 |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i119t-c4be9ad61c4dc0056c1128e7f3247e1cb272de0732cec58e7fbea4197dc85e0d3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Sep 27 05:40:30 EDT 2023 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i119t-c4be9ad61c4dc0056c1128e7f3247e1cb272de0732cec58e7fbea4197dc85e0d3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_10253070 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-July-19 |
| PublicationDateYYYYMMDD | 2023-07-19 |
| PublicationDate_xml | – month: 07 year: 2023 text: 2023-July-19 day: 19 |
| PublicationDecade | 2020 |
| PublicationTitle | 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) |
| PublicationTitleAbbrev | ICECCME |
| PublicationYear | 2023 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.8677948 |
| Snippet | The privacy-preserving federated learning (FL) algorithm is considered one of the most widely used distributed training algorithms. Its effectiveness is... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Computational modeling Enhance Performance FedAvg Federated learning FedProx Measurement Mechatronics Modified Algorithms Prediction algorithms Predictive models Training |
| Title | Accelerating Federated Learning via Modified Local Model Update Based on Individual Performance Metric |
| URI | https://ieeexplore.ieee.org/document/10253070 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09b8IwELVK1aFTW5Wq3_LQNTROnDge2whUBhBDkdiQfb6gSFWCKPD76wsB1KFDt9hRZMnn0zs7fu8x9mKkdTILTWASkwbSiDiwmfBbFSjiEJzTWWobswk1HmezmZ60ZPWGC4OIzeUz7NFj8y_f1bChozKf4VFCa7TDOkqlO7JWKyQqQv06zPt5Pur7NRiHPbIF7-0_-GWd0iDH4OKfY16y7pGDxycHdLliJ1hds-INwOMERa1a8AEJQfha0fFWJXXBt6Xho9qVRUm9hFPUxC8-XdLWnr970HK8rvjwQMTikyN3gI_IYAu6bDrof-YfQeuUEJRC6HUA0qI2LhUgHZC6J_gyKkNV-HJJoQAbqcihz-YIEBJ6YdFIoZWDLMHQxTfstKorvGW8SG1srQpF4YHbGG0TI32gXaSjFH3y37EuTdJ8uRPDmO_n5_6P_gd2TqGg41ChH9nperXBJ3YG23X5vXpuQvgDEx2fxw |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFA46BT2pOPG3OXjtbPo7Ry0bG65jhw12G8nL6yhIO-a2v9-82jk8ePDWpJRAXh7fS5rv-xh7VoE2QeIqR4UqcgIlfEcnwm5VIPddMEYmka7NJuLRKJnN5Lghq9dcGESsL59hhx7rf_mmgg0dldkM90Jao4fsiKyzGrpWIyUqXPkySLtpmnXtKvTdDhmDd3af_DJPqbGjd_bPUc9Ze8_C4-MffLlgB1hesvwVwCIFxa1c8B5JQdhq0fBGJ3XBt4XiWWWKvKBeQipq4gefLmlzz98sbBlelXzwQ8Xi4z17gGdksQVtNu11J2nfabwSnEIIuXYg0CiViQQEBkjfE2whlWCc24IpRgHaiz2DNp89QAjphUYVCBkbSEJ0jX_FWmVV4jXjeaR9rWNX5Ba6lZI6VIENtfGkF6FN_xvWpkmaL7_lMOa7-bn9o_-JnfQn2XA-HIze79gphYUOR4W8Z631aoMP7Bi26-Jz9ViH8wuTB6MQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2023+3rd+International+Conference+on+Electrical%2C+Computer%2C+Communications+and+Mechatronics+Engineering+%28ICECCME%29&rft.atitle=Accelerating+Federated+Learning+via+Modified+Local+Model+Update+Based+on+Individual+Performance+Metric&rft.au=Barhoush%2C+Mahdi&rft.au=Ayad%2C+Ahmad&rft.au=Schmeink%2C+Anke&rft.date=2023-07-19&rft.pub=IEEE&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FICECCME57830.2023.10253070&rft.externalDocID=10253070 |