190 citations · 566 across the 19 of their papers we have counts for
22 papers
Can Generative Retrievers Learn Semantic IDs Without Forgetting How to Speak?
Junchen Fu, Kleomenis Katevas, Vandana Rajan +2
Generative retrieval (GR) enables end-to-end retrieval by generating document semantic identifiers (SIDs). However, retrieval-only fine-tuning can over-specialize pretrained langua…
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,…
Context-Aware Membership Inference Attacks against Pre-trained Large Language Models
Hongyan Chang, Ali Shahin Shamsabadi, Kleomenis Katevas +2
Membership Inference Attacks (MIAs) on pre-trained Large Language Models (LLMs) aim at determining if a data point was part of the model's training set. Prior MIAs that are built f…
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…
FLaaS: Cross-App On-device Federated Learning in Mobile Environments
Kleomenis Katevas, Diego Perino, Nicolas Kourtellis
Federated Learning (FL) has recently emerged as a popular solution to distributedly train a model on user devices improving user privacy and system scalability. Major Internet comp…
BatteryLab: A Collaborative Platform for Power Monitoring
Matteo Varvello, Kleomenis Katevas, Mihai Plesa +3
Advances in cloud computing have simplified the way that both software development and testing are performed. This is not true for battery testing for which state of the art test-b…
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…
PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments
Fan Mo, Hamed Haddadi, Kleomenis Katevas +3
We propose and implement a Privacy-preserving Federated Learning () framework for mobile systems to limit privacy leakages in federated learning. Leveraging the widespread pr…
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…
Policy-Based Federated Learning
Kleomenis Katevas, Eugene Bagdasaryan, Jason Waterman +4
In this paper we present PoliFL, a decentralized, edge-based framework that supports heterogeneous privacy policies for federated learning. We evaluate our system on three use case…
BatteryLab, A Distributed Power Monitoring Platform For Mobile Devices
Matteo Varvello, Kleomenis Katevas, Mihai Plesa +2
Recent advances in cloud computing have simplified the way that both software development and testing are performed. Unfortunately, this is not true for battery testing for which s…
Towards Characterizing and Limiting Information Exposure in DNN Layers
Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas +2
Pre-trained Deep Neural Network (DNN) models are increasingly used in smartphones and other user devices to enable prediction services, leading to potential disclosures of (sensiti…
Finding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensing
Kleomenis Katevas, Katrin Hänsel, Richard Clegg +3
Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish. The ability to automatically detect and remember these types of inte…
Typical Phone Use Habits: Intense Use Does Not Predict Negative Well-Being
Kleomenis Katevas, Ioannis Arapakis, Martin Pielot
Not all smartphone owners use their device in the same way. In this work, we uncover broad, latent patterns of mobile phone use behavior. We conducted a study where, via a dedicate…
Deep Private-Feature Extraction
Seyed Ali Osia, Ali Taheri, Ali Shahin Shamsabadi +3
We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model which is trained and evaluated based on information theoretic constraints. Using the selective exchange…
Continual Prediction of Notification Attendance with Classical and Deep Network Approaches
Kleomenis Katevas, Ilias Leontiadis, Martin Pielot +1
We investigate to what extent mobile use patterns can predict -- at the moment it is posted -- whether a notification will be clicked within the next 10 minutes. We use a data set…
Privacy-Preserving Deep Inference for Rich User Data on The Cloud
Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri +4
Deep neural networks are increasingly being used in a variety of machine learning applications applied to rich user data on the cloud. However, this approach introduces a number of…
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…
SensingKit: Evaluating the Sensor Power Consumption in iOS devices
Kleomenis Katevas, Hamed Haddadi, Laurissa Tokarchuk
Today's smartphones come equipped with a range of advanced sensors capable of sensing motion, orientation, audio as well as environmental data with high accuracy. With the existenc…