IEEE Internet Computing 
2023 to appear

Impact Factor: 5.277

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

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.


Pre-camera PDF 

IEEE Library Access

BibTeX:
@article{Ottun:IC2023,
  author={Ottun, Abdul-Rasheed and Mane, Pramod and Yin, Zhang and Paul, Souvik and Liyanage, Mohan and Pridmore, Jason and Ding, Aaron Yi and Sharma, Rajesh and Nurmi, Petteri and Flores, Huber},
  journal={IEEE Internet Computing}, 
  title={Social-aware Federated Learning: Challenges and Opportunities in Collaborative Data Training}, 
  year={2023}
}
How to cite:

Abdul-Rasheed Ottun, Pramod C. Mane, Zhigang Yin, Souvik Paul, Mohan Liyanage, Jason Pridmore, Aaron Yi Ding, Rajesh Sharma, Petteri Nurmi, Huber Flores, "Social-aware Federated Learning: Challenges and Opportunities in Collaborative Data Training", in IEEE Internet Computing, 2023 (to appear)