77 citations · 171 across the 24 of their papers we have counts for
5 papers · 1 filter
Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settings
Marco Arazzi, Mert Cihangiroglu, Antonino Nocera
Federated Averaging remains the most widely used aggregation strategy in federated learning due to its simplicity and scalability. However, its performance degrades significantly i…
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning
Marco Arazzi, Stefanos Koffas, Antonino Nocera +1
Federated Transfer Learning (FTL) is the most general variation of Federated Learning. According to this distributed paradigm, a feature learning pre-step is commonly carried out b…
KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
Marco Arazzi, Serena Nicolazzo, Antonino Nocera
Vertical Federated Learning (VFL) is a category of Federated Learning in which models are trained collaboratively among parties with vertically partitioned data. Typically, in a VF…
Label Inference Attacks against Node-level Vertical Federated GNNs
Marco Arazzi, Mauro Conti, Stefanos Koffas +4
Federated learning enables collaborative training of machine learning models by keeping the raw data of the involved workers private. Three of its main objectives are to improve th…
Turning Privacy-preserving Mechanisms against Federated Learning
Marco Arazzi, Mauro Conti, Antonino Nocera +1
Recently, researchers have successfully employed Graph Neural Networks (GNNs) to build enhanced recommender systems due to their capability to learn patterns from the interaction b…