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

Department of Media and Communication

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Pages (from-to)	1-7
Number of pages	7
Journal	IEEE Internet Computing
DOIs	https://doi.org/10.1109/MIC.2022.3219263
Publication status	Published - 3 Nov 2022

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AU - Ding, Aaron Yi

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

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