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Found 9 papers, 1 papers with code
Initialisation and Topology Effects in Decentralised Federated Learning
Fully decentralised federated learning enables collaborative training of individual machine learning models on distributed devices on a network while keeping the training data localised.
Impact of network topology on the performance of Decentralized Federated Learning
We highlight the challenges in transferring knowledge from peripheral to central nodes, attributed to a dilution effect during model aggregation.
Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity
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 incomplete data representation.
Exploring the Impact of Disrupted Peer-to-Peer Communications on Fully Decentralized Learning in Disaster Scenarios
Thus, fully decentralized learning can help in this case.
The effect of network topologies on fully decentralized learning: a preliminary investigation
Specifically, we highlight the different roles in this process of more or less connected nodes (hubs and leaves), as well as that of macroscopic network properties (primarily, degree distribution and modularity).
Structural invariants and semantic fingerprints in the "ego network" of words
In this respect, ring #1 can be seen as the semantic fingerprint of the ego network of words.
Weak Signals in the Mobility Landscape: Car Sharing in Ten European Cities
Car sharing is one the pillars of a smart transportation infrastructure, as it is expected to reduce traffic congestion, parking demands and pollution in our cities.
Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks
In order to validate this claim, we focus on popular feature-extraction prediction algorithms (both unsupervised and supervised) and we extend them to include social-circles awareness.
Car sharing through the data analysis lens
Car sharing is one the pillars of a smart transportation infrastructure, as it is expected to reduce traffic congestion, parking demands and pollution in our cities.
