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Found 16 papers, 1 papers with code
Humans-in-the-Building: Getting Rid of Thermostats for Optimal Thermal Comfort Control in Energy Management Systems
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.
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
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.
VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning
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.
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation
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.
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization
Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms.
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus
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.
Can Competition Outperform Collaboration? The Role of Misbehaving Agents
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.
Self-triggered MPC robust to bounded packet loss via a min-max approach: extended version
Networked Control Systems typically come with a limited communication bandwidth and thus require special care when designing the underlying control and triggering law.
Competition-Based Resilience in Distributed Quadratic Optimization
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.
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing
There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL).
Transmission Power Allocation for Remote Estimation with Multi-packet Reception Capabilities
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.
Optimal Network Topology of Multi-Agent Systems subject to Computation and Communication Latency (with proofs)
We study minimum-variance feedback-control design for a networked control system with retarded dynamics, where inter-agent communication is subject to latency.
Accelerated Probabilistic Power Flow in Electrical Distribution Networks via Model Order Reduction and Neumann Series Expansion
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.
Smart Grid State Estimation with PMUs Time Synchronization Errors
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
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering
In this work we study the non-parametric reconstruction of spatio-temporal dynamical Gaussian processes (GPs) via GP regression from sparse and noisy data.
Multi-agents adaptive estimation and coverage control using Gaussian regression
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.
