Social-Aware Federated Learning: Challenges and Opportunities in Collaborative Data Training
Abdul-Rasheed Olatunji Ottun, Pramod C. Mane, Zhigang Yin, Souvik Paul, Mohan Liyanage, Jason Pridmore, Aaron Yi Ding, Rajesh R. Sharma, Petteri Nurmi, Huber Flores · IEEE Internet Computing · 2022
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.