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

1 Citation (Scopus)
29 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)36-44
Number of pages9
JournalIEEE Internet Computing
Volume27
Issue number2
DOIs
Publication statusPublished - 2022

Keywords

  • Analytical models
  • Artificial intelligence
  • Collaboration
  • Data Collection
  • Data models
  • Device-to-Device
  • Federated learning
  • Incentives
  • Task analysis
  • Training

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