CommunityAI: Towards Community-based Federated Learning
This work addresses scalability and heterogeneity issues in Federated Learning for community applications, but it is incremental as it builds on existing FL concepts without introducing a new paradigm.
The paper tackles the challenge of adapting Federated Learning for community domains by proposing CommunityAI, a framework that organizes participants into communities based on shared interests or data characteristics to collaboratively train models while preserving privacy, though no concrete results or numbers are provided.
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.