1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
Optimizing Privacy-Utility Trade-off in Decentralized Learning with Generalized Correlated Noise
Angelo Rodio, Zheng Chen, Erik G. Larsson
Decentralized learning enables distributed agents to collaboratively train a shared machine learning model without a central server, through local computation and peer-to-peer comm…
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…
Federated Learning under Heterogeneous and Correlated Client Availability
Angelo Rodio, Francescomaria Faticanti, Othmane Marfoq +2
The enormous amount of data produced by mobile and IoT devices has motivated the development of federated learning (FL), a framework allowing such devices (or clients) to collabora…