Federated learning (FedL) is a machine learning (ML) technique utilized to train deep neural networks (DeepNNs) in a distributed way without the need to share data among the federated training clients. FedL was proposed for edge computing and Internet of things (IoT) tasks in which a centralized...
01a Articolo in rivista
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Federated Learning (FL) is an enabling technology for Machine Learning in scenarios in which it is impossible, for privacy and/or regulatory reasons, to analyze data in a centralized manner. FL envisages that distributed clients cooperate to learn a model without any data exchange, in favor of a...
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This paper presents the first framework (up to the authors' knowledge) to address time-varying objectives in finite-horizon Deep Reinforcement Learning (DeepRL), based on a switching control solution developed on the ground of Bellman's principle of optimality. By augmenting the state space of the...
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One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (...
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We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the ‘motion flux’, a quantity which captures the motion variation of a group of...
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We study the effects of the deregulation of intercity bus services in Europe on intermodal competition in long-distance land passenger transport and on social welfare. We consider a bus company with a flexible (Internet-based) business model and a rail operator with a rigid cost structure due to...
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Emergency department (ED) overcrowding is a well-recognized worldwide phenomenon which affects the quality of emergency care. A direct consequence of overcrowding is a long wait for visit and treatment of people who require primary care, possibly endangering the lives of critical patients....
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We introduce a class of positive definite preconditioners for the solution of large symmetric indefinite linear systems or sequences of such systems, in optimization frameworks. The preconditioners are iteratively constructed by collecting information on a reduced eigenspace of the indefinite...
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In this work, we deal with Truncated Newton methods for solving large scale (possibly nonconvex) unconstrained optimization problems. In particular,we consider the use of amodified Bunch and Kaufman factorization for solving the Newton equation, at each (outer) iteration of the method. The Bunch...
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This paper includes a twofold result for the Nonlinear Conjugate Gradient (NCG) method, in large scale unconstrained optimization. First we consider a theoretical analysis, where preconditioning is embedded in a strong convergence framework of an NCG method from the literature. Mild conditions to...