Scalable Computing Systems Laboratory

Computing systems that make human sense of big data are now ubiquitous. Equipped with powerful AI algorithms, they are now present in all aspects of our life: they drive cars, do surgery, control the lighting in your home, recommend movies and books, and are even about to replace banks.

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Beyond the AI algorithms and the availability of large volume of data, this is now possible because we can  scale systems to thousand, even millions of distributed entities. Yet, designing efficient distributed systems come with many challenges that we address in our team.
We are interested in all aspects of such large-scale distributed systems be they datacenters, edge computing, fully decentralized systems, self organizing systems, and we are working on scalable design,  failure resilience, performance and  privacy-preservation.

Our current research interests include:
System support for machine learning
Federated Learning Systems
Large-scale recommenders
Graph-based systems
Privacy-aware recommendation systems
Collaborative computing


December 2020

Prof. Anne-Marie Kermarrec, head of SaCS, and her collaborators have received the Middleware 2020 Best Paper Award for “Fleet: Online Federated Learning via Staleness Awareness and Performance Prediction” [Arxiv]:

November 2020

Rafael Pires, Post-Doc in SaCS, received the Léon Du Pasquier and Louis Perrier Prize for “an excellent PhD dissertation in one of the scientific domains of the Faculty of Science” of University of Neuchâtel, for his dissertation: “Distributed systems and trusted execution environments: Trade-offs and challenges“.