CommunityAI: Towards Community-based Federated Learning

TL;DR

The paper addresses the challenge of applying Federated Learning to diverse community domains with data heterogeneity and privacy constraints. It proposes CommunityAI, a three-tier edge-cloud framework that organizes participants into FL communities and autonomous cohorts based on shared interests, data characteristics, and contexts. Key contributions include a conceptual architecture, definitions of notations and metadata, a mechanism to form FL populations and cohorts, and a client-server architecture with both server-side and client-side components. The work outlines potential applications in Wellness, industrial automation, healthcare, and computing continuum systems, and enumerates future research directions such as metadata protocols, adaptive filtering, and privacy-preserving enhancements.

Abstract

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.

Figures (4)

• Figure 1: Community domains.
• Figure 2: An overview of wearable ECD and their benefits.
• Figure 3: FL within CommunityAI.
• Figure 4: The conceptual architecture and workflow of CommunityAI.