Keynote Lectures

Federated Learning (FED) of eXplainable Artificial Intelligence (XAI) Models
Pietro Ducange, University of Pisa, Italy

Advances in Temporal and Spatio-Temporal Data Management and Analytics
Christian S. Jensen, Aalborg University, Denmark

Abstract
The current era is characterized by an increasing pervasiveness of applications and services based on data processing and often built on Artificial Intelligence (AI) and, in particular, Machine Learning (ML) algorithms. In fact, extracting insights from data is so common in daily life of individuals, companies, and public entities and so relevant for the market players, to become an important matter of interest for institutional organizations. The topic is so important and hot that ad hoc guidelines and regulations for designing trustworthy AI-based applications have also been proposed by the European Union and other national and supra-national bodies. One important aspect is given by the capability of the applications to tackle the data privacy issue.  Additionally, depending on the specific application field, paramount importance is given to the possibility for the humans to understand why a certain AI/ML-based application is providing that specific output.
Trustworthy AI models should be trained with the simultaneous goals of preserving the data privacy and ensuring a certain level of explainabilty of the system. In this talk, we discuss the concept of Federated Learning (FL) of eXplainable AI (XAI) models, in short FED-XAI, purposely designed to address the two requirements simultaneously.
We first introduce the motivations at the foundation of FL and XAI, along with their basic concepts. Then, we provide a brief survey regarding approaches, models, results, issues and applications on FED-XAI. Finally, we also show our recently released framework for providing user-friendly support to Federated Learning (FL) of Fuzzy Rule-Based Systems (FRBS) as explainable-by-design models.

Abstract
I examine my research stream from when I started and up until today. In a world focused on administrative data, I first worked on temporal databases, considering aspects such as query language design, indexing, and query processing. When, around the year 2000, we saw the emergence of a mobile revolution, I started to work on so-called “moving objects,” covering again indexing and query processing, as well as other topics such as privacy. A decade later, geo-social services were taking off, and geo-tagged text content was beginning to proliferate. That prompted work on spatio-textual indexing and query processing to support spatial keyword querying. Later, we witnessed the increasing availability of vehicle trajectory data, which then prompted work on foundations for applications of vehicle trajectory data, most prominently in relation to vehicle routing. Most recently, partly in response to the increased deployment of sensor technologies and digitalization of societal and industrial processes, I started working on analytics for spatially correlated time series data. In the talk, I will cover key topics I have worked on by means of examples, I will explain why I worked on those topics and how I have tried to balance continuity and renewal, and I will examine the impact of the research. I hope this will prompt the participants to reflect on their own research streams, past and future.