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

Research output: Contribution to journalArticleScientificpeer-review


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 languageEnglish
JournalIEEE Internet Computing
Issue number2
Pages (from-to)36-44
Number of pages9
Publication statusPublished - Mar 2023
MoE publication typeA1 Journal article-refereed

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Fields of Science

  • 113 Computer and information sciences
  • 516 Educational sciences

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