15 citations · 59 across the 31 of their papers we have counts for
5 papers · 1 filter
DecHW: Heterogeneous Decentralized Federated Learning Exploiting Second-Order Information
Adnan Ahmad, Chiara Boldrini, Lorenzo Valerio +2
Decentralized Federated Learning (DFL) is a serverless collaborative machine learning paradigm where devices collaborate directly with neighbouring devices to exchange model inform…
The Built-In Robustness of Decentralized Federated Averaging to Bad Data
Samuele Sabella, Chiara Boldrini, Lorenzo Valerio +2
Decentralized federated learning (DFL) enables devices to collaboratively train models over complex network topologies without relying on a central controller. In this setting, loc…
Robustness of Decentralised Learning to Nodes and Data Disruption
Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio +3
In the vibrant landscape of AI research, decentralised learning is gaining momentum. Decentralised learning allows individual nodes to keep data locally where they are generated an…
Impact of network topology on the performance of Decentralized Federated Learning
Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio +2
Fully decentralized learning is gaining momentum for training AI models at the Internet's edge, addressing infrastructure challenges and privacy concerns. In a decentralized machin…
Exploring the Impact of Disrupted Peer-to-Peer Communications on Fully Decentralized Learning in Disaster Scenarios
Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio +2
Fully decentralized learning enables the distribution of learning resources and decision-making capabilities across multiple user devices or nodes, and is rapidly gaining popularit…