5 papers · 1 filter
Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations
Adrián Sánchez-Mompó, Ioannis Mavromatis, Peizheng Li +2
This study presents an empirical investigation into the energy consumption of Discriminative and Generative AI models within real-world MLOps pipelines. For Discriminative models,…
FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments
Ioannis Mavromatis, Stefano De Feo, Aftab Khan
This paper presents Federated Learning with Adaptive Monitoring and Elimination (FLAME), a novel solution capable of detecting and mitigating concept drift in Federated Learning (F…
FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning
Alexander Herzog, Robbie Southam, Ioannis Mavromatis +1
Federated Learning (FL) is a distributed machine learning approach that enables training on decentralized data while preserving privacy. However, FL systems often involve resource-…
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…