1 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
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…