  Fully decentralised federated learning enables collaborative training of
individual machine learning models on distributed devices on a communication
network while keeping the training data localised. This approach enhances data
privacy and eliminates both the single point of failure and the necessity for
central coordination. Our research highlights that the effectiveness of
decentralised federated learning is significantly influenced by the network
topology of connected devices. We propose a strategy for uncoordinated
initialisation of the artificial neural networks, which leverages the
distribution of eigenvector centralities of the nodes of the underlying
communication network, leading to a radically improved training efficiency.
Additionally, our study explores the scaling behaviour and choice of
environmental parameters under our proposed initialisation strategy. This work
paves the way for more efficient and scalable artificial neural network
training in a distributed and uncoordinated environment, offering a deeper
understanding of the intertwining roles of network structure and learning
dynamics.
