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Found 5 papers, 1 papers with code
FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning
First, we design an inexactness criterion for the clients' local updates to eliminate the need for empirically setting the local training accuracy.
Approximate and Weighted Data Reconstruction Attack in Federated Learning
Federated Learning (FL) is a distributed learning paradigm that enables multiple clients to collaborate on building a machine learning model without sharing their private data.
The Hard-Constraint PINNs for Interface Optimal Control Problems
We show that the physics-informed neural networks (PINNs), in combination with some recently developed discontinuity capturing neural networks, can be applied to solve optimal control problems subject to partial differential equations (PDEs) with interfaces and some control constraints.
Accelerated primal-dual methods with enlarged step sizes and operator learning for nonsmooth optimal control problems
The accelerated primal-dual method with operator learning is mesh-free, numerically efficient, and scalable to different types of PDEs.
Bilinear Optimal Control of an Advection-reaction-diffusion System
However, due to the additional divergence-free constraint on the control variable and the nonlinear relation between the state and control variables, it is challenging to compute the gradient and the optimal stepsize at each CG iteration, and thus nontrivial to implement the CG method.
Optimization and Control 49M41, 35Q93, 49J20
