190 citations · 566 across the 19 of their papers we have counts for
8 papers · 1 filter
AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices
Dzung Pham, Kleomenis Katevas, Ali Shahin Shamsabadi +1
Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi-step tasks such as coding or web-based question answering. While remote,…
MELTing point: Mobile Evaluation of Language Transformers
Stefanos Laskaridis, Kleomenis Katevas, Lorenzo Minto +1
Transformers have revolutionized the machine learning landscape, gradually making their way into everyday tasks and equipping our computers with "sparks of intelligence". However,…
P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups
Ioannis Arapakis, Panagiotis Papadopoulos, Kleomenis Katevas +1
Distributed (or Federated) learning enables users to train machine learning models on their very own devices, while they share only the gradients of their models usually in a diffe…
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
Dusan Stamenkovic, Alexandros Karatzoglou, Ioannis Arapakis +2
Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms…
FLaaS: Federated Learning as a Service
Nicolas Kourtellis, Kleomenis Katevas, Diego Perino
Federated Learning (FL) is emerging as a promising technology to build machine learning models in a decentralized, privacy-preserving fashion. Indeed, FL enables local training on…
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas +4
We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neur…
Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions
Kleomenis Katevas, Ilias Leontiadis, Martin Pielot +1
We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping,…
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics
Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh +5
Internet of Things (IoT) devices and applications are being deployed in our homes and workplaces. These devices often rely on continuous data collection to feed machine learning mo…