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

Information and Communication Technology

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

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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.

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 - 2022

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/MIC.2022.3219263

Social-aware_Federated_Learning_Challenges_and_Opportunities_in_Collaborative_Data_TrainingAccepted author manuscript, 2.64 MBLicence: CC BY-NC-ND

Cite this

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title = "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.",

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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

PY - 2022

Y1 - 2022

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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.

KW - Analytical models

KW - Artificial intelligence

KW - Collaboration

KW - Data Collection

KW - Data models

KW - Device-to-Device

KW - Federated learning

KW - Incentives

KW - Task analysis

KW - Training

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

Abstract

Keywords

UN SDGs

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