Can I read Federated Graph Classification over Non-IID Graphs on EtoBox?

Federated Graph Classification over Non-IID Graphs by Xie, Han; Ma, Jing; Xiong, Li; Yang, Carl is a scholarly article available to read on EtoBox.

What is Federated Graph Classification over Non-IID Graphs about?

Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can also be regarded as a special type of data samples, which can be collected and stored in separate local systems. Similar to other domains, multiple local systems, each holding a small set of graphs, may benefit from collaboratively training a powerful graph mining model, such as the popular graph neural networks (GNNs). To provide more motivation towards such endeavors, we analyze real-world graphs from different domains to confirm that they indeed share certain graph properties that are statistically significant compared with random graphs. However, we also find that different sets of graphs, even from the same domain or same dataset, are non-IID regarding both graph structures and node features. To handle this, we propose a graph clustered federated learning (GCFL) framework that dynamically finds clusters of local systems based on the gradients of GNNs, and theoretically justify that such clusters can reduce the structure and feature heterogeneity among graphs owned by the local systems. Moreover, we obs

Author

Xie, Han; Ma, Jing; Xiong, Li; Yang, Carl

Published

2021

Language

EN