Social-aware Federated Learning: Challenges and Opportunities in Collaborative Data Training

Abdul-Rasheed  Ottun; Pramod C.  Mane; Zhigang Yin; Souvik  Paul; Mohan Liyanage; Jason  Pridmore; Aaron Yi Ding; Rajesh Sharma; Petteri Nurmi; Huber Flores

doi:10.1109/MIC.2022.3219263

Social-aware Federated Learning: Challenges and Opportunities in Collaborative Data Training

Abdul-Rasheed Ottun, Pramod C. Mane, Zhigang Yin, Souvik Paul, Mohan Liyanage, Jason Pridmore, Aaron Yi Ding, Rajesh Sharma, Petteri Nurmi, Huber Flores

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Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

Sammanfattning

Federated learning (FL) is a promising privacy-preserving solution to build powerful AI models. In many FL scenarios, such as healthcare or smart city monitoring, the user's devices may lack the required capabilities to collect suitable data which limits their contributions to the global model. We contribute social-aware federated learning as a solution to boost the contributions of individuals by allowing outsourcing tasks to social connections. We identify key challenges and opportunities, and establish a research roadmap for the path forward. Through a user study with N = 30 participants, we study collaborative incentives for FL showing that social-aware collaborations can significantly boost the number of contributions to a global model provided that the right incentive structures are in place.

Originalspråk	engelska
Tidskrift	IEEE Internet Computing
Volym	27
Nummer	2
Sidor (från-till)	36-44
Antal sidor	9
ISSN	1089-7801
DOI	https://doi.org/10.1109/MIC.2022.3219263
Status	Publicerad - mars 2023
MoE-publikationstyp	A1 Tidskriftsartikel-refererad

Bibliografisk information

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”

Vetenskapsgrenar

113 Data- och informationsvetenskap
516 Pedagogik

Åtkomst av dokument

10.1109/MIC.2022.3219263Licens: CC BY-NC-ND

Social_Aware_Federated_Machine_Learning_IEEE_Internet_ComputingAccepterat manuskript från författare, 2,63 MBLicens: !!Other
FeatureSlutlig publicerad version, 1,38 MBLicens: CC BY-NC-ND

Foundations of Pervasive Sensing Systems
Nurmi, P. (Principal Investigator)
01/09/2021 → 31/08/2025
Projekt: Finlands Akademi: Akademiprojektsbidrag

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abstract = "Federated learning (FL) is a promising privacy-preserving solution to build powerful AI models. In many FL scenarios, such as healthcare or smart city monitoring, the user's devices may lack the required capabilities to collect suitable data which limits their contributions to the global model. We contribute social-aware federated learning as a solution to boost the contributions of individuals by allowing outsourcing tasks to social connections. We identify key challenges and opportunities, and establish a research roadmap for the path forward. Through a user study with N = 30 participants, we study collaborative incentives for FL showing that social-aware collaborations can significantly boost the number of contributions to a global model provided that the right incentive structures are in place.",

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Social-aware Federated Learning: Challenges and Opportunities in Collaborative Data Training

Sammanfattning

Bibliografisk information

Vetenskapsgrenar

Åtkomst av dokument

Projekt

Foundations of Pervasive Sensing Systems

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