2 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
Disentangling degree and tie strength heterogeneity in egocentric social networks
Sara Heydari, Gerardo Iñiguez, János Kertész +1
The structure of personal networks reflects how we organise and maintain social relationships. The distribution of tie strengths in personal networks is heterogeneous, with a few c…
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…
Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity
Lorenzo Valerio, Chiara Boldrini, Andrea Passarella +3
Federated Learning (FL) is a well-known framework for successfully performing a learning task in an edge computing scenario where the devices involved have limited resources and in…