Zobrazeno 1 - 10
of 829
pro vyhledávání: '"JOHANSSON, KARL H."'
In this paper, we depart from the widely-used gradient descent-based hierarchical federated learning (FL) algorithms to develop a novel hierarchical FL framework based on the alternating direction method of multipliers (ADMM). Within this framework,
Externí odkaz:
http://arxiv.org/abs/2409.18796
This paper proposes an observer-based formation tracking control approach for multi-vehicle systems with second-order motion dynamics, assuming that vehicles' relative or global position and velocity measurements are unavailable. It is assumed that a
Externí odkaz:
http://arxiv.org/abs/2409.08675
Distributed learning has become the standard approach for training large-scale machine learning models across private data silos. While distributed learning enhances privacy preservation and training efficiency, it faces critical challenges related t
Externí odkaz:
http://arxiv.org/abs/2409.08640
This paper studies community detection for a nonlinear opinion dynamics model from its equilibria. It is assumed that the underlying network is generated from a stochastic block model with two communities, where agents are assigned with community lab
Externí odkaz:
http://arxiv.org/abs/2409.08004
In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we
Externí odkaz:
http://arxiv.org/abs/2409.05418
On final opinions of the Friedkin-Johnsen model over random graphs with partially stubborn community
This paper studies the formation of final opinions for the Friedkin-Johnsen (FJ) model with a community of partially stubborn agents. The underlying network of the FJ model is symmetric and generated from a random graph model, in which each link is a
Externí odkaz:
http://arxiv.org/abs/2409.05063
For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns arise from disclosing sensitive measurements to a cloud estimator. To solve this issu
Externí odkaz:
http://arxiv.org/abs/2408.17263
In this paper we address distributed learning problems over peer-to-peer networks. In particular, we focus on the challenges of quantized communications, asynchrony, and stochastic gradients that arise in this set-up. We first discuss how to turn the
Externí odkaz:
http://arxiv.org/abs/2408.17156
In this paper, we introduce a temporal logic-based safety filter for Autonomous Intersection Management (AIM), an emerging infrastructure technology for connected vehicles to coordinate traffic flow through intersections. Despite substantial work on
Externí odkaz:
http://arxiv.org/abs/2408.14870
Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data. However, this paradigm introduces new privacy and security risks, with ma
Externí odkaz:
http://arxiv.org/abs/2408.08628