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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, ...)
SUBmission guidelines
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.
Workshop paper submissions should not exceed 8 pages (including references). 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.
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.
Submissions will be evaluated by at least three reviewers on the basis of relevance, technical quality, potential impact, and clarity. The reviewing process is double-blind (reviewers and area chairs are not aware of the identities of the authors; reviewers can see each other’s names). Papers must not include identifying information of the authors (names, affiliations, etc.), self-references, or links (e.g., GitHub, YouTube) that reveal the authors’ identities (e.g., references to own work should be given neutrally like other references, not mentioning ‘our previous work’ or similar). However, we recognize there are limits to what is feasible with respect to anonymization. For example, if you use data from your own organization and it is relevant to the paper to name this organization, you may do so.
Electronic submissions will be handled via the CMT platform. For a new submission, click the button below.
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