CommunityAI: Towards Community-based Federated Learning
Ilir Murturi Praveen Kumar Donta Schahram Dustdar
Federated Learning (FL) has emerged as a promising paradigm to train machine
learning models collaboratively while preserving data privacy. However, its
widespread adoption faces several challenges, including scalability,
heterogeneous data and devices, resource constraints, and security concerns.
Despite its promise, FL has not been specifically adapted for community
domains, primarily due to the wide-ranging differences in data types and
context, devices and operational conditions, environmental factors, and
stakeholders. In response to these challenges, we present a novel framework for
Community-based Federated Learning called CommunityAI. CommunityAI enables
participants to be organized into communities based on their shared interests,
expertise, or data characteristics. Community participants collectively
contribute to training and refining learning models while maintaining data and
participant privacy within their respective groups. Within this paper, we
discuss the conceptual architecture, system requirements, processes, and future
challenges that must be solved. Finally, our goal within this paper is to
present our vision regarding enabling a collaborative learning process within
various communities.