TY - JOUR
T1 - Social-aware Federated Learning
T2 - Challenges and Opportunities in Collaborative Data Training
AU - Ottun, Abdul Rasheed
AU - Mane, Pramod C.
AU - Yin, Zhigang
AU - Paul, Souvik
AU - Liyanage, Mohan
AU - Pridmore, Jason
AU - Ding, Aaron Yi
AU - Sharma, Rajesh
AU - Nurmi, Petteri
AU - Flores, Huber
N1 - Publisher Copyright:
Author
PY - 2022/11/3
Y1 - 2022/11/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85141554035&partnerID=8YFLogxK
U2 - 10.1109/MIC.2022.3219263
DO - 10.1109/MIC.2022.3219263
M3 - Article
AN - SCOPUS:85141554035
SN - 1089-7801
VL - 27
SP - 1
EP - 7
JO - IEEE Internet Computing
JF - IEEE Internet Computing
IS - 2
ER -