FedCBO: Reaching group consensus in clustered federated learning through consensus-based optimization
Federated learning is an important framework in modern machine learning that seeks to
integrate the training of learning models from multiple users, each user having their own …
integrate the training of learning models from multiple users, each user having their own …
Leveraging memory effects and gradient information in consensus-based optimisation: On global convergence in mean-field law
K Riedl - European Journal of Applied Mathematics, 2024 - cambridge.org
In this paper, we study consensus-based optimisation (CBO), a versatile, flexible and
customisable optimisation method suitable for performing nonconvex and nonsmooth global …
customisable optimisation method suitable for performing nonconvex and nonsmooth global …
Consensus-based optimisation with truncated noise
Consensus-based optimisation (CBO) is a versatile multi-particle metaheuristic optimisation
method suitable for performing non-convex and non-smooth global optimisations in high …
method suitable for performing non-convex and non-smooth global optimisations in high …
CBX: Python and Julia packages for consensus-based interacting particle methods
We introduce CBXPy and ConsensusBasedX. jl, Python and Julia implementations of
consensus-based interacting particle systems (CBX), which generalise consensus-based …
consensus-based interacting particle systems (CBX), which generalise consensus-based …
A particle consensus approach to solving nonconvex-nonconcave min-max problems
We propose a zero-order optimization method for sequential min-max problems based on
two populations of interacting particles. The systems are coupled so that one population …
two populations of interacting particles. The systems are coupled so that one population …
Pypop7: A pure-python library for population-based black-box optimization
In this paper, we present an open-source pure-Python library called PyPop7 for black-box
optimization (BBO). As population-based methods (eg, evolutionary algorithms, swarm …
optimization (BBO). As population-based methods (eg, evolutionary algorithms, swarm …
A PDE Framework of Consensus-Based Optimization for Objectives with Multiple Global Minimizers
M Fornasier, L Sun - arXiv preprint arXiv:2403.06662, 2024 - arxiv.org
Consensus-based optimization (CBO) is an agent-based derivative-free method for non-
smooth global optimization that has been introduced in 2017, leveraging a surprising …
smooth global optimization that has been introduced in 2017, leveraging a surprising …
A multiscale Consensus-Based algorithm for multi-level optimization
A novel multiscale consensus-based optimization (CBO) algorithm for solving bi-and tri-level
optimization problems is introduced. Existing CBO techniques are generalized by the …
optimization problems is introduced. Existing CBO techniques are generalized by the …
An interacting particle consensus method for constrained global optimization
This paper presents a particle-based optimization method designed for addressing
minimization problems with equality constraints, particularly in cases where the loss function …
minimization problems with equality constraints, particularly in cases where the loss function …
A consensus-based algorithm for non-convex multiplayer games
In this paper, we present a novel consensus-based zeroth-order algorithm tailored for non-
convex multiplayer games. The proposed method leverages a metaheuristic approach using …
convex multiplayer games. The proposed method leverages a metaheuristic approach using …