3 papers
cs.LG2025
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…
cs.LG2024
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…
cs.DB2020
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…