By hosting this workshop at SIGKDD, we hope to promote the development of federated learning as a scientific field and encourage data mining researchers and practitioners to collaborate on this important research topic. We envision the workshop will bring in an overall advancement of FL and broaden its impact in the data mining research and industrial community, while FL has become an increasingly important tool for data mining in recent years. The workshop will cover the foundation and advanced problems in FL within the below three categories.
1. Scalability of FL facing heterogeneous data and heterogeneous computation or communication resources, including
* Learning with a large scale of clients;
* Learning from heterogeneous data distributions;
* Learning from heterogeneous hardware or computation capabilities;
* High-dimensional feature selection from distributed clients;
* Learning under unreliable network conditions.
2. Trustworthiness. Problems and solutions for the security, privacy, and social alignment of FL systems, including
* Privacy leakage during training and inference;
* Uncertainty in data collection with noisy labels or data;
* Backdoor attacks;
* Data poisoning.
3. Applications. Explorations on novel research problems of FL and FL algorithms for real-world applications, including
* Bioinformatics and biomedical informatics;
* Financial engineering and quantitative finance;
* Medical imaging;
* Drug discovery;
* Social networks and graph-based learning;
* Natural language processing;
* Computer vision.
Moreover, we aim to attract high-quality original research of federated learning with applications, evaluation, and algorithms. We also plan to invite open discussions on controversial yet crucial topics regarding FL systems and discuss their barriers in data mining.

Submission Guidelines

We invite short technical papers - up to 5 pages including references and unlimited pages of appendix. All manuscripts should be submitted in a single PDF file including all content, figures, tables, and references, following the new Standard ACM Conference Proceedings Template.

For LaTeX users: unzip acmart.zip, make, and use sample-sigconf.tex as a template; Additional information about formatting and style files is available online at ACM Proceedings Template (https://www.acm.org/publications/proceedings-template). Additionally, papers must be in the two-column format, with the recommended setting for Latex file: \documentclass[sigconf, review]{acmart}.

Papers should be submitted to https://openreview.net/group?id=KDD.org/2023/Workshop/FL4Data-Mining

All papers will be double-blinded and peer-reviewed by at least 2 reviewers.

While all accepted papers will be presented with posters, high-quality accepted papers will also have the opportunity to participate in the oral/spotlight presentation and win our Best Paper Award(s).
We will also present accepted papers on our website.
According to the policy of the KDD conference, the accepted papers will NOT be included in proceedings or any form of publication.