7 papers
Learning to Fine-tune Foundation Models under Resource Limitations
Thomas Tsouparopoulos, Iordanis Koutsopoulos
We study the problem of optimal continual fine-tuning for a pre-trained Foundation Model deployed at a resource-limited device. At each time slot, a new batch of training data arri…
Personalized Federated Learning with Exact Stochastic Gradient Descent
Sotirios Nikoloutsopoulos, Iordanis Koutsopoulos, Michalis K. Titsias
We propose a Stochastic Gradient Descent (SGD)-type algorithm for Personalized Federated Learning which can be particularly attractive for mobile energy-limited regimes due to its…
Video Quality Monitoring for Remote Autonomous Vehicle Control
Dimitrios Kafetzis, Nikos Fotiou, Savvas Argyropoulos +2
The delivery of high-quality, low-latency video streams is critical for remote autonomous vehicle control, where operators must intervene in real time. However, reliable video deli…
Large Language Model Partitioning for Low-Latency Inference at the Edge
Dimitrios Kafetzis, Ramin Khalili, Iordanis Koutsopoulos
Large Language Models (LLMs) based on autoregressive, decoder-only Transformers generate text one token at a time, where a token represents a discrete unit of text. As each newly p…
Collaborative Split Federated Learning with Parallel Training and Aggregation
Yiannis Papageorgiou, Yannis Thomas, Alexios Filippakopoulos +2
Federated learning (FL) operates based on model exchanges between the server and the clients, and it suffers from significant client-side computation and communication burden. Spli…
Explainability and Continual Learning meet Federated Learning at the Network Edge
Thomas Tsouparopoulos, Iordanis Koutsopoulos
As edge devices become more capable and pervasive in wireless networks, there is growing interest in leveraging their collective compute power for distributed learning. However, op…