Talk title: Distributed Analytics for Data Streams
Abstract: Big Data and the Internet of Things (IoT) have the potential to fundamentally shift the way we interact with our surroundings. The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analytics of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in stream mining. In this talk, I will present an overview of data stream analytics, and I will introduce some popular open source tools for data stream analytics.

Talk title: Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
Abstract: We consider the fully decentralized machine learning scenario where many users with personal datasets collaborate to learn models through local peer-to-peer exchanges, without a central coordinator. We propose to train personalized models that leverage a collaboration graph describing the relationships between the users' personal tasks, which we learn jointly with the models. Our fully decentralized optimization procedure alternates between training nonlinear models given the graph in a greedy boosting manner, and updating the collaboration graph (with controlled sparsity) given the models. Throughout the process, users exchange messages only with a small number of peers (their direct neighbors in the graph and a few random users), ensuring that the procedure naturally scales to large numbers of users. We analyze the convergence rate, memory and communication complexity of our approach, and demonstrate its benefits compared to competing techniques on synthetic and real datasets.

Talk title: Distributed and Democratized Machine Learning at the Edge
Abstract: Distributed artificial intelligence (AI) have proliferated in the recent years by the potential of federated learning (FL) in cross-device distributed machine learning applications. FL leverages a collaborative model training approach where distributed learning agents build a common global model without sharing their raw dataset. In practice, FL often deals with large-scale, highly personalized, and unbalanced data. This triggers the challenge of FL in improving the personalized learning performance together with a high level of generalization capability for their agents. This article explores the democratized learning (Dem-AI) paradigm to design a decentralized control and aggregation methodology in regions by leveraging distributed multi-access edge computing (MEC) platforms in the future large-scale distributed learning systems. The use of regional MEC servers to construct regional models, instead at the remote cloud, not only minimizes the overhead and communication loads, but also ensures scalability of future distributed learning applications. Research opportunities and challenges are discussed, highlighting on edge-assisted Dem-AI operations to fill the shortcomings in FL for large-scale distributed learning systems.

Talk title: Harnessing the Computing Continuum for Urgent Science
Abstract: Urgent science describes time-critical, data-driven scientific workflows that can leverage distributed data sources in a timely way to facilitate important decision making. In spite of the exponential growth of available digital data sources and the ubiquity of non-trivial computational power for processing this data, realizing such urgent science workflows remains challenging -- while our capacity for generating data is expanding dramatically, our ability for managing, analyzing, and transforming this data into knowledge in a timely manner has not kept pace. In this talk I will explore how the computing continuum, spanning resources at the edges, in the core and in-between, can be harnessed to support urgent science. Using an Early Earthquake Warning (EEW) workflow, which combines data streams from geo-distributed seismometers and GPS sensors to detect tsunamis, as a driver, I will explore a system stack that can enable the fluid integration of distributed analytics across a dynamic infrastructure spanning the computing continuum and discuss associated research challenges. I will describe recent research in programming abstractions that can express what data should be processed and when and where it should be processed, middleware services that automate the discovery of resources and the orchestration of computations across these resources. I will also discuss open research challenges.