This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...
Recent developments in network neuroscience have highlighted the importance of developing techniques for analysing and modelling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use ...
Machine learning is currently shifting from a centralized paradigm to decentralized ones where machine learning models are trained collaboratively. In fully decentralized learning algorithms, data remains where it was produced, models are trained locally a ...
Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization ch ...
This paper studies the problem of inferring whether an agent is directly influenced by another agent over a network. Agent i influences agent j if they are connected (according to the network topology), and if agent j uses the data from agent i to update i ...
This work examines the problem of learning the topology of a network (graph learning) from the signals produced at a subset of the network nodes (partial observability). This challenging problem was recently tackled assuming that the topology is drawn acco ...
Real-time control of electric grids is a novel approach to handling the increasing penetration of distributed and volatile energy generation brought about by renewables. Such control occurs in cyber-physical systems (CPSs), in which software agents maintai ...
Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers fro ...
This work presents new secondary regulators for current sharing and voltage balancing in DC microgrids, composed of distributed generation units, dynamic RLC lines, and nonlinear ZIP (constant impedance, constant current, and constant power) loads. The pro ...
Distributed graph signal processing methods require that the graph nodes communicate by exchanging messages. These messages have a finite precision in a realistic network, which may necessitate to implement quantization. Quantization, in turn, generates er ...
