2 citations · 3 across the 11 of their papers we have counts for
12 papers
No More Guessing: a Verifiable Gradient Inversion Attack in Federated Learning
Francesco Diana, Chuan Xu, André Nusser +1
Gradient inversion attacks threaten client privacy in federated learning by reconstructing training samples from clients' shared gradients. Gradients aggregate contributions from m…
A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communication
Angelo Rodio, Giovanni Neglia, Zheng Chen +1
In semi-decentralized federated learning, devices primarily rely on device-to-device communication but occasionally interact with a central server. Periodically, a sampled subset o…
Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling
Daniel Richards Arputharaj, Charlotte Rodriguez, Angelo Rodio +1
Training large-scale machine learning models incurs substantial carbon emissions. Federated Learning (FL), by distributing computation across geographically dispersed clients, offe…
Reconciling Communication Compression and Byzantine-Robustness in Distributed Learning
Diksha Gupta, Antonio Honsell, Chuan Xu +2
Distributed learning enables scalable model training over decentralized data, but remains hindered by Byzantine faults and high communication costs. While both challenges have been…
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
Francesco Diana, André Nusser, Chuan Xu +1
Federated Learning (FL) enables collaborative training of machine learning models across distributed clients without sharing raw data, ostensibly preserving data privacy. Neverthel…
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble
Jacopo Talpini, Marco Savi, Giovanni Neglia
One-Shot Federated Learning (FL) is a recent paradigm that enables multiple clients to cooperatively learn a global model in a single round of communication with a central server.…
Streaming Federated Learning with Markovian Data
Tan-Khiem Huynh, Malcolm Egan, Giovanni Neglia +1
Federated learning (FL) is now recognized as a key framework for communication-efficient collaborative learning. Most theoretical and empirical studies, however, rely on the assump…
Attribute Inference Attacks for Federated Regression Tasks
Francesco Diana, Othmane Marfoq, Chuan Xu +3
Federated Learning (FL) enables multiple clients, such as mobile phones and IoT devices, to collaboratively train a global machine learning model while keeping their data localized…
Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments
Charlotte Rodriguez, Laura Degioanni, Laetitia Kameni +2
Monitoring, understanding, and optimizing the energy consumption of Machine Learning (ML) are various reasons why it is necessary to evaluate the energy usage of ML. However, there…
FedStale: leveraging stale client updates in federated learning
Angelo Rodio, Giovanni Neglia
Federated learning algorithms, such as FedAvg, are negatively affected by data heterogeneity and partial client participation. To mitigate the latter problem, global variance reduc…
Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks
Caelin Kaplan, Angelo Rodio, Tareq Si Salem +2
As Internet of Things (IoT) technology advances, end devices like sensors and smartphones are progressively equipped with AI models tailored to their local memory and computational…
A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses
Caelin G. Kaplan, Chuan Xu, Othmane Marfoq +2
Within the realm of privacy-preserving machine learning, empirical privacy defenses have been proposed as a solution to achieve satisfactory levels of training data privacy without…