[CFP] Deadline Extended - ECML-PKDD 2024 - 2nd Workshop on Advancements in Federated Learning (WAFL)

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Mirko Polato

Jun 14, 2024, 12:49:33 PMJun 14
to Machine Learning News

2nd Workshop on Advancements in Federated Learning (WAFL)

Paper submission: June 21, 2024
Notification of acceptance: July 15, 2024
Final paper submission: TBA
Conference: September 9 - 13, 2024


AI-based systems, especially those based on machine learning technologies, have become central in modern societies. In the meanwhile, users and legislators are becoming aware of privacy issues. Users are increasingly reluctant to share their sensitive information, and new laws have been enacted to regulate how private data is handled (e.g., the GDPR).

Federated Learning (FL) has been proposed to develop better AI systems without compromising users’ privacy and the legitimate interests of private companies. Although still in its infancy, FL has already shown significant theoretical and practical results making FL one of the hottest topics in the machine learning community.

Given the considerable potential in overcoming the challenges of protecting users’ privacy while making the most of available data, we propose WAFL (Workshop on Advancements in Federated Learning Technologies) at ECML-PKDD 2024.

This workshop aims to focus the attention of the ECML-PKDD research community on addressing the open questions and challenges in this thriving research area. Given the broad range of competencies in the ECML-PKDD community, the workshop will welcome foundational contributions and contributions expanding the scope of these techniques, such as improvements in the interpretability and fairness of the learned models.

The WAFL workshop will be centered on the theme of improving and studying the Federated Learning setting. It will welcome applicative and theoretical contributions as well as contributions about specific settings and benchmarking tools. The topics include (but are not limited to):

- Algorithmic and theoretical advances in FL
- Federated Learning with non-iid data distributions
- Security and privacy of FL systems (e.g., differential privacy, adversarial attacks, poisoning attacks, inference attacks, data anonymization, model distillation, secure multi-party computation ...)
- Other non-functional properties of FL (e.g., fairness, interpretability/explainability, personalization ...)
- FL variants and Decentralized Federated Learning (e.g., vertical FL, split-learning, gossip learning, ...)
- Applications of FL (e.g., FL for healthcare, FL on edge devices, advertising, social network, blockchain, web search ...)
- Tools and resources (e.g., benchmark datasets, software libraries, ...)

We invite submissions of original research on all aspects of Federated Learning (see the not complete list of topics above). Each accepted paper will be included in the workshop proceedings (published by Springer Communications in Computer and Information Science) and presented in the talk session. Authors will have the faculty to opt-in or opt-out.

Workshop paper submissions should not exceed not exceed 12 pages (excluding references) shorter papers are also welcome. Papers must be self-contained, written in English, and formatted according to the Springer LNCS guidelines. Author instructions, style files, and the copyright form can be downloaded here.

The reviewing process is double-blind so all papers need to be "best-effort" anonymized. We strongly encourage making code and data available anonymously (e.g., in an anonymous GitHub repository via Anonymous GitHub or in a Dropbox folder). The authors may have a (non-anonymous) pre-print published online, but it should not be cited in the submitted paper to preserve anonymity. Reviewers will be asked not to search for them.

Dr. Mirko Polato, Department of Computer Science, University of Turin, Italy
Prof. Roberto Esposito, Department of Computer Science, University of Turin, Italy
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