69 citations · 70 across the 5 of their papers we have counts for
5 papers
Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inference
Ioannis Mavromatis, Kostas Katsaros, Aftab Khan
Machine learning (ML) has seen tremendous advancements, but its environmental footprint remains a concern. Acknowledging the growing environmental impact of ML this paper investiga…
Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems
Jikun Gao, Ioannis Mavromatis, Peizheng Li +2
Federated learning (FL) systems face performance challenges in dealing with heterogeneous devices and non-identically distributed data across clients. We propose a dynamic global m…
FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power Capping Evaluation
Ioannis Mavromatis, Stefano De Feo, Pietro Carnelli +2
The Open Radio Access Network (O-RAN) is a burgeoning market with projected growth in the upcoming years. RAN has the highest CAPEX impact on the network and, most importantly, con…
FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments
Theo Chow, Usman Raza, Ioannis Mavromatis +1
Intelligent, large-scale IoT ecosystems have become possible due to recent advancements in sensing technologies, distributed learning, and low-power inference in embedded devices.…
An Intrusion Detection System based on Deep Belief Networks
Othmane Belarbi, Aftab Khan, Pietro Carnelli +1
The rapid growth of connected devices has led to the proliferation of novel cyber-security threats known as zero-day attacks. Traditional behaviour-based IDS rely on DNN to detect…