no code implementations • 12 Mar 2024 • Jiali Wang, Yang Tang, Luca Schenato
Given the widespread attention to individual thermal comfort, coupled with significant energy-saving potential inherent in energy management systems for optimizing indoor environments, this paper aims to introduce advanced "Humans-in-the-building" control techniques to redefine the paradigm of indoor temperature design.
no code implementations • 19 Feb 2024 • Arman Adibi, Nicolo Dal Fabbro, Luca Schenato, Sanjeev Kulkarni, H. Vincent Poor, George J. Pappas, Hamed Hassani, Aritra Mitra
Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling.
no code implementations • 30 Nov 2023 • Luca Ballotta, Nicolò Dal Fabbro, Giovanni Perin, Luca Schenato, Michele Rossi, Giuseppe Piro
In this domain, federated learning is one of the most effective and promising techniques for training global machine learning models, while preserving data privacy at the vehicles and optimizing communications resource usage.
no code implementations • 29 Sep 2023 • Alessio Maritan, Subhrakanti Dey, Luca Schenato
Federated learning is a distributed learning framework that allows a set of clients to collaboratively train a model under the orchestration of a central server, without sharing raw data samples.
no code implementations • 18 May 2023 • Nicolò Dal Fabbro, Michele Rossi, Luca Schenato, Subhrakanti Dey
Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms.
no code implementations • 13 May 2023 • Alessio Maritan, Ganesh Sharma, Luca Schenato, Subhrakanti Dey
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 optimization and information exchange between neighbouring nodes.
no code implementations • 4 Jul 2022 • Luca Ballotta, Giacomo Como, Jeff S. Shamma, Luca Schenato
We investigate a novel approach to resilient distributed optimization with quadratic costs in a multi-agent system prone to unexpected events that make some agents misbehave.
no code implementations • 1 Apr 2022 • Stefan Wildhagen, Matthias Pezzutto, Luca Schenato, Frank Allgöwer
Networked Control Systems typically come with a limited communication bandwidth and thus require special care when designing the underlying control and triggering law.
no code implementations • 26 Mar 2022 • Luca Ballotta, Giacomo Como, Jeff S. Shamma, Luca Schenato
This paper proposes a novel approach to resilient distributed optimization with quadratic costs in a networked control system (e. g., wireless sensor network, power grid, robotic team) prone to external attacks (e. g., hacking, power outage) that cause agents to misbehave.
no code implementations • 11 Feb 2022 • Nicolò Dal Fabbro, Subhrakanti Dey, Michele Rossi, Luca Schenato
There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL).
no code implementations • 29 Jan 2021 • Matthias Pezzutto, Luca Schenato, Subhrakanti Dey
In this paper we consider the problem of transmission power allocation for remote estimation of a dynamical system in the case where the estimator is able to simultaneously receive packets from multiple interfering sensors, as it is possible e. g. with the latest wireless technologies such as 5G and WiFi.
no code implementations • 25 Jan 2021 • Luca Ballotta, Mihailo R. Jovanović, Luca Schenato
We study minimum-variance feedback-control design for a networked control system with retarded dynamics, where inter-agent communication is subject to latency.
no code implementations • 28 Oct 2020 • Samuel Chevalier, Luca Schenato, Luca Daniel
This subspace is used to construct and update a reduced order model (ROM) of the full nonlinear system, resulting in a highly efficient simulation for future voltage profiles.
1 code implementation • 26 Nov 2019 • Marco Todescato, Ruggero Carli, Luca Schenato, Grazia Barchi
We consider the problem of PMU-based state estimation combining information coming from ubiquitous power demand time series and only a limited number of PMUs.
Optimization and Control Systems and Control Systems and Control
no code implementations • 3 May 2017 • Marco Todescato, Andrea Carron, Ruggero Carli, Gianluigi Pillonetto, Luca Schenato
In this work we study the non-parametric reconstruction of spatio-temporal dynamical Gaussian processes (GPs) via GP regression from sparse and noisy data.
no code implementations • 22 Jul 2014 • Andrea Carron, Marco Todescato, Ruggero Carli, Luca Schenato, Gianluigi Pillonetto
We consider a scenario where the aim of a group of agents is to perform the optimal coverage of a region according to a sensory function.