93 citations · 375 across the 30 of their papers we have counts for
3 papers · 1 filter
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization
Nicolò Dal Fabbro, Michele Rossi, Luca Schenato +1
Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms. These are, in fact, desirable qualities for DO frameworks, such a…
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus
Alessio Maritan, Ganesh Sharma, Luca Schenato +1
This paper considers the problem of distributed multi-agent learning, where the global aim is to minimize a sum of local objective (empirical loss) functions through local optimiza…
ZO-JADE: Zeroth-order Curvature-Aware Multi-Agent Convex Optimization
Alessio Maritan, Luca Schenato
In this work we address the problem of convex optimization in a multi-agent setting where the objective is to minimize the mean of local cost functions whose derivatives are not av…