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.

我们提出了一种名为 CommunityAI 的用于基于社区的联邦学习的新框架，通过共享兴趣、专业知识或数据特征来组织参与者，并在各自的群组中维护数据和参与者的隐私，从而使参与者能够共同贡献于训练和完善学习模型的过程中。