Graph Neural Networks (GNNs) are a new and increasingly popular family of deep neural network architectures to perform learning on graphs. Training them efficiently is challenging due the irregular nature graph data. The problem becomes even more when scaling large graphs that exceed capacity single devices. Standard approaches distributed DNN training, such as data model parallelism, do not di...

Abstract In this paper, we address the problem of privacy-preserving distributed learning and evaluation machine-learning models by analyzing it in widespread MapReduce abstraction that extend with privacy constraints. We design spindle (Scalable Privacy-preservINg Distributed LEarning), first system covers complete ML workflow enabling execution a cooperative gradient-descent obtained model pr...

Many developments in science and engineering depend on tackling complex optimizations large scales. The challenge motivates intense search for specific computing hardware that takes advantage from quantum features, nonlinear dynamics, or photonics. A paradigmatic optimization problem is finding low-energy states classical spin systems with fully-random interactions. To date no alternative platf...