Many popular web-based services and data mining applications nowadays leverage the power of machine learning (ML) and artificial intelligence (AI) to ensure effective performance. All of these are made possible because of the huge volume of data constantly generated on various devices, such as PCs/laptops and mobile smartphones.

Centralized ML and AI pose significant challenges due to regulatory and privacy concerns in real-world use cases. Privacy has been traditionally viewed as an essential human right. There have been increasing legislation endeavors on data privacy protection, e.g. European Union General Data Protection Regulation and California Consumer Privacy Act.

Federated learning (FL) is a new paradigm in machine learning that was first introduced by Google in 2017. It aims to address the challenges above by training a global model using distributed data, without the need for the data to be shared nor transferred to any central facility. Despite the clear advantages, there are still many technical challenges waiting to be solved, such as fairness issues, data statistical heterogeneity, communication efficiency and network robustness.

The workshop is targeted on the above and other relevant issues, aiming to create a platform for people from academia and industry to communicate their insights and recent results.

Topics of interest include, but are not limited to, the following:
FL algorithm related issues, e.g. adversarial attack, communication compression, algorithm explainability/interpretability, data/device heterogeneity, optimization algorithm advances, personalization, fairness, resource efficiency, and so on;
FL and collaborative ML applications, like advertising, query analysis and processing, web healthcare, search engine, log mining, recommender system, blockchain，social network, and others;
Other data privacy preservation techniques, such as differential privacy, secure multi-party computing, data/model distillation, data anonymization, etc;
Social, operational challenges and legislation issues about privacy in web search and data mining;
Datasets and open-source tools for federated and privacy-preserving web search and data mining.