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Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneities
Arash Badie-Modiri, Chiara Boldrini, Lorenzo Valerio +2
Decentralised federated learning, based on peer-to-peer communication, is increasingly proposed for on-device training of machine learning models, promising a privacy-preserving, c…
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…
Initialisation and Network Effects in Decentralised Federated Learning
Arash Badie-Modiri, Chiara Boldrini, Lorenzo Valerio +2
Fully decentralised federated learning enables collaborative training of individual machine learning models on a distributed network of communicating devices while keeping the trai…
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…