4 citations · 7 across the 5 of their papers we have counts for
6 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…
Multilayer network science: theory, methods, and applications
Alberto Aleta, Andreia Sofia Teixeira, Guilherme Ferraz de Arruda +14
Multilayer network science has emerged as a central framework for analysing interconnected and interdependent complex systems. Its relevance has grown substantially with the increa…
Opinion dynamics: Statistical physics and beyond
Michele Starnini, Fabian Baumann, Tobias Galla +5
Opinion dynamics, the study of how individual beliefs and collective public opinion evolve, is a fertile domain for applying statistical physics to complex social phenomena. Like p…
A Comparative Analysis of Wealth Index Predictions in Africa between three Multi-Source Inference Models
Márton Karsai, János Kertész, Lisette Espín-Noboa
Poverty map inference has become a critical focus of research, utilizing both traditional and modern techniques, ranging from regression models to convolutional neural networks app…
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…