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

doi:10.1109/MIC.2022.3219263

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

Department of Media and Communication

Research output: Contribution to journal › Article › Academic › peer-review

6 Citations (Scopus)

89 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 language	English
Pages (from-to)	36-44
Number of pages	9
Journal	IEEE Internet Computing
Volume	27
Issue number	2
DOIs	https://doi.org/10.1109/MIC.2022.3219263
Publication status	Published - 1 Mar 2023

Bibliographical note

Funding Information:
This research was part of the SPATIAL project that has received funding from the European Union's Horizon 2020 research and innovation programme under Grant 101021808. European Social Fund has also financed this research via "ICT programme" measure, CHIST-ERA Grant CHIST-ERA-19-XAI-010, and the Academy of Finland under Projects 317875 and 339614.

Publisher Copyright:
© 1997-2012 IEEE.

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Social-aware Federated Learning Challenges andFinal published version, 2.64 MBLicence: CC BY-NC-ND

Cite this

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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.",

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AU - Liyanage, Mohan

AU - Pridmore, Jason

AU - Ding, Aaron Yi

AU - Sharma, Rajesh

AU - Nurmi, Petteri

AU - Flores, Huber

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

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