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Title	Social-Aware Federated Learning: Challenges And Opportunities In Collaborative Data Training
ID_Doc	52072
Authors	Ottun A.-R.; Mane P.C.; Yin Z.; Paul S.; Liyanage M.; Pridmore J.; Ding A.Y.; Sharma R.; Nurmi P.; Flores H.
Year	2023
Published	IEEE Internet Computing, 27, 2
DOI	http://dx.doi.org/10.1109/MIC.2022.3219263
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. © 1997-2012 IEEE.
Author Keywords	Data Collection; Device-to-Device; Incentives

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