Social-aware Federated Learning
Challenges and Opportunities in Collaborative Data Training
Abdul Rasheed Ottun (University of Tartu)
Pramod C. Mane (Indian Institute of Management Rohtak)
Zhigang Yin (University of Tartu)
Souvik Paul (University of Tartu)
Mohan Liyanage (University of Tartu)
Jason Pridmore ( Erasmus Universiteit Rotterdam)
Aaron Yi Ding (TU Delft - Technology, Policy and Management)
Rajesh Sharma (University of Tartu)
Petteri Nurmi (University of Helsinki)
Huber Flores (University of Tartu)
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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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