System and method for privacy-preserving distributed training of machine learning models on distributed datasets

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We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

The problem of Byzantine resilience in distributed machine learning, a.k.a., Byzantine machine learning, consists in designing distributed algorithms that can train an accurate model despite the presence of Byzantine nodes, i.e., nodes with corrupt data or ...

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Humans and animals constantly adapt to their environment over the course of their life. This thesis seeks to integrate various timescales of adaptation, ranging from the adaptation of synaptic connections between spiking neurons (milliseconds), rapid behav ...

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global neural network model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being commun ...

This study describes and evaluates a new downscaling scheme that specifically addresses the need for hillslope-scale atmospheric-forcing time series for modelling the local impact of regional climate change projections on the land surface in complex terrai ...

As in oceans, large-scale coherent circulations such as gyres and eddies are ubiquitous features in large lakes that are subject to the Coriolis force. They play a crucial role in the horizontal and vertical distribution of biological, chemical and physica ...

Near-surface wind is difficult to estimate using global numerical weather and climate models, because airflow is strongly modified by underlying topography, especially that of a country such as Switzerland. In this article, we use a statistical approach ba ...

System and method for privacy-preserving distributed training of machine learning models on distributed datasets

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Bayes-optimal Learning of Deep Random Networks of Extensive-width

Byzantine Machine Learning: A Primer

RMAML: Riemannian meta-learning with orthogonality constraints

Universal and adaptive methods for robust stochastic optimization

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Scalable and Privacy-Preserving Federated Principal Component Analysis

Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning

From event-based surprise to lifelong learning. A journey in the timescales of adaptation

System and method for privacy-preserving distributed training of neural network models on distributed datasets

TopoCLIM: rapid topography-based downscaling of regional climate model output in complex terrain v1.1

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Downscaling of Historical Wind Fields over Switzerland Using Generative Adversarial Networks