3 citations · 4 across the 7 of their papers we have counts for
7 papers
Contribution-Aware Bandwidth Allocation for Multimodal Split Learning
Iason Ofeidis, Leandros Tassiulas
Multimodal models are increasingly the default option for perception at the network edge, yet they are trained almost entirely in the datacenter, because a client holding several s…
CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification
Iason Ofeidis, Nikos Papadis, Randeep Bhatia +2
The rapid expansion of the Internet of Things (IoT) and Industrial IoT (IIoT) has created a massive, heterogeneous attack surface that challenges traditional network security mecha…
FedJam: Multimodal Federated Learning Framework for Jamming Detection
Ioannis Panitsas, Iason Ofeidis, Leandros Tassiulas
Jamming attacks pose a critical threat to wireless networks, yet existing detection methods remain largely unimodal, centralized and resource-intensive, limiting their performance,…
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency
Akrit Mudvari, Antero Vainio, Iason Ofeidis +2
The growing number of AI-driven applications in mobile devices has led to solutions that integrate deep learning models with the available edge-cloud resources. Due to multiple ben…
Leveraging Machine Learning for Multichain DeFi Fraud Detection
Georgios Palaiokrassas, Sandro Scherrers, Iason Ofeidis +1
Since the inception of permissionless blockchains with Bitcoin in 2008, it became apparent that their most well-suited use case is related to making the financial system and its ad…
Monetary Policy, Digital Assets, and DeFi Activity
Antzelos Kyriazis, Iason Ofeidis, Georgios Palaiokrassas +1
This paper studies the effects of unexpected changes in US monetary policy on digital asset returns. We use event study regressions and find that monetary policy surprises negative…
An Overview of the Data-Loader Landscape: Comparative Performance Analysis
Iason Ofeidis, Diego Kiedanski, Leandros Tassiulas
Dataloaders, in charge of moving data from storage into GPUs while training machine learning models, might hold the key to drastically improving the performance of training jobs. R…