Federated Learning is machine learning in the context of a network of clients whilst maintaining data residency and/or privacy constraints. Community detection is the unsupervised discovery of clusters of nodes within graph-structured data. The intersection of these two fields uncovers much opportunity, but also challenge. For example, it adds complexity due to missing connectivity information between privately held graphs. In this work, we explore the potential of federated community detection by conducting initial experiments across a range of existing datasets that showcase the gap in performance introduced by the distributed data. We demonstrate that isolated models would benefit from collaboration establishing a framework for investigating challenges within this domain. The intricacies of these research frontiers are discussed alongside proposed solutions to these issues.

翻译：联邦学习是在客户网络环境下实现机器学习，同时满足数据驻留和/或隐私约束的技术。社区检测是在图结构数据中无监督地发现节点聚类。这两个领域的交叉既带来诸多机遇，也面临挑战。例如，由于私有图之间缺少连接信息，问题复杂度显著增加。本研究通过在一系列现有数据集上进行初步实验，探索联邦社区检测的潜力，揭示分布式数据导致的性能差距。我们证明，孤立模型若建立协同框架将受益于此，该框架可用于探究该领域内的关键问题。本文在讨论这些研究前沿复杂性的同时，提出了相应的解决方案。