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.

翻译：联邦学习（Federated Learning, FL）已成为一种在保护数据隐私的同时协作训练机器学习模型的有前景范式。然而，其广泛应用仍面临可扩展性、异构数据与设备、资源约束及安全性等多重挑战。尽管潜力巨大，FL尚未针对社区领域进行专门适配，主要原因在于数据类型与上下文、设备与运行条件、环境因素及利益相关者之间存在广泛差异。针对这些挑战，我们提出了一种名为CommunityAI的新型社区联邦学习框架。CommunityAI使参与者能够基于共同兴趣、专业知识或数据特征组织成社区。社区参与者共同贡献于学习模型的训练与优化，同时在各自群体内维护数据与参与者隐私。本文讨论了概念架构、系统需求、流程及未来需解决的关键挑战。最终，本文旨在阐述我们对推动不同社区间协作学习过程的愿景。