6 papers
Integrity of peer-to-peer distributed LLM inference under malicious nodes
Mert Cihangiroglu, Antonino Nocera
Peer-to-peer distributed inference executes a Large Language Model (LLM) on pooled consumer hardware by spreading its layers across many nodes. Every request passes through nodes t…
An AI-Based Solution for Secure Service Provisioning in IoT
Marco Arazzi, Mert Cihangiroglu, Serena Nicolazzo +2
As the Internet of Things (IoT) continues its rapid expansion, the attack surface grows accordingly, with emerging threats targeting smart objects and their interactions. In this e…
LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections
Mert Cihangiroglu, Alessandro Pegoraro, Phillip Rieger +2
Split learning (SL) enables collaborative training by partitioning a neural network across clients and a central server, but the cut-layer interface introduces a key challenge: hig…
Security in LLM-as-a-Judge: A Comprehensive SoK
Aiman Al Masoud, Antony Anju, Marco Arazzi +6
LLM-as-a-Judge (LaaJ) is a novel paradigm in which powerful language models are used to assess the quality, safety, or correctness of generated outputs. While this paradigm has sig…
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…
Secure Federated Data Distillation
Marco Arazzi, Mert Cihangiroglu, Serena Nicolazzo +1
Dataset Distillation (DD) is a powerful technique for reducing large datasets into compact, representative synthetic datasets, accelerating Machine Learning training. However, trad…