2 citations · 3 across the 11 of their papers we have counts for
6 papers · 1 filter
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…