This workshop has been completed.

Overview

The field of graph data mining, one of the most important AI research areas, has been revolutionized by graph neural networks (GNNs), which benefit from training on real-world graph data with millions to billions of nodes and links. Unfortunately, the training data and process of GNNs involving graphs beyond millions of nodes are extremely costly on a centralized server, if not impossible. Moreover, due to the increasing concerns about data privacy, emerging data from realistic applications are naturally fragmented, forming distributed private graphs of multiple “data silos”, among which direct transferring of data is forbidden. The nascent field of federated learning (FL), which aims to enable individual clients to jointly train their models while keeping their local data decentralized and completely private, is a promising paradigm for large-scale distributed and private training of GNNs.

The FedGraph workshop aims to bring together researchers from different backgrounds with a common interest in how to extend current FL algorithms to operate with graph data models such as GNNs. FL is an extremely hot topic of large commercial interest and has been intensively explored for machine learning with visual and textual data. The exploration from graph mining researchers and industrial practitioners is timely catching up just recently. There are many unexplored challenges and opportunities, which urges the establishment of an organized and open community to collaboratively advance the science behind it. The prospective participants of this workshop will include researchers and practitioners from both graph mining and federated learning communities, whose interests include, but are not limited to: graph analysis and mining, heterogeneous network modeling, complex data mining, large-scale machine learning, distributed systems, optimization, meta-learning, reinforcement learning, privacy, robustness, explainability, fairness, ethics, and trustworthiness.

Call for Submissions

We invite participation in the 1st International Workshop on Federated Learning with Graph Data (FedGraph), to be held as part of the CIKM2022 conference. Please check Submission for more information about the topics and submission instructions.

Best Paper Award(s)

With pride and gratitude, we announce that we will have several Best Paper Awards (with cash prizes), and possibly some travel awards, sponsored by JZTData Technology, Tencent, FedML, and USC-Amazon Center.

Important Dates

Submission: August 15, 11:59pm AoE
Notification: September 15
Camera-ready: October 15
Workshop date: October 21