3 papers
cs.LG2026
A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions
Antonios Makris, Christos Dousis, Emmanouil Kritharakis +2
Federated Learning has emerged as a transformative paradigm for collaborative machine learning across distributed environments. However, its performance is strongly influenced by t…
cs.LG2026
Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering
Emmanouil Kritharakis, Dusan Jakovetic, Antonios Makris +1
Federated Learning (FL) enables collaborative model training across multiple clients without sharing private data. We consider FL scenarios wherein FL clients are subject to advers…
cs.LG2025
FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning
Emmanouil Kritharakis, Antonios Makris, Dusan Jakovetic +1
Federated Learning (FL) enables collaborative model training across multiple clients while preserving data privacy by keeping local datasets on-device. In this work, we address FL…