5 papers
FDA-Opt: Federated Fine-Tuning via Dynamic Update Schedules
Michael Theologitis, Vasilis Samoladas, Antonios Deligiannakis
Federated Learning (FL) enables the utilization of vast, previously inaccessible data sources. At the same time, pre-trained Language Models (LMs) have taken the world by storm and…
Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging
Michail Theologitis, Georgios Frangias, Georgios Anestis +2
The ever-growing volume and decentralized nature of data, coupled with the need to harness it and extract knowledge, have led to the extensive use of distributed deep learning (DDL…
A Synopses Data Engine for Interactive Extreme-Scale Analytics
Antonis Kontaxakis, Nikos Giatrakos, Antonios Deligiannakis
In this work, we detail the design and structure of a Synopses Data Engine (SDE) which combines the virtues of parallel processing and stream summarization towards delivering inter…
Sketch-based Querying of Distributed Sliding-Window Data Streams
Odysseas Papapetrou, Minos Garofalakis, Antonios Deligiannakis
While traditional data-management systems focus on evaluating single, ad-hoc queries over static data sets in a centralized setting, several emerging applications require (possibly…
Discovering Attractive Products based on Influence Sets
Anastasios Arvanitis, Antonios Deligiannakis
Skyline queries have been widely used as a practical tool for multi-criteria decision analysis and for applications involving preference queries. For example, in a typical online r…