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MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods
Gradient-type distributed optimization methods have blossomed into one of the most important tools for solving a minimization learning task over a networked agent system.
Enhance Local Consistency in Federated Learning: A Multi-Step Inertial Momentum Approach
Federated learning (FL), as a collaborative distributed training paradigm with several edge computing devices under the coordination of a centralized server, is plagued by inconsistent local stationary points due to the heterogeneity of the local partial participation clients, which precipitates the local client-drifts problems and sparks off the unstable and slow convergence, especially on the aggravated heterogeneous dataset.
Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation
Then, based on the distributed optimisation algorithm, an output regulation method is utilised to solve the optimal coordination problem for general linear dynamic systems.
Data-Driven Load-Current Sharing Control for Multi-Stack Fuel Cell System with Circulating Current Mitigation
The Multi-stack Fuel Cell System (MFCS), which is an assembly of FC stacks, can be a remedy for obstacles in high-power applications.
Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement
This paper presents a novel scheme termed Optimization-based Ramping Reserve Allocation (ORRA) for addressing an ongoing challenge in Automatic Generation Control (AGC) enhancement, i. e., the optimal coordination of multiple Battery Energy Storage Systems (BESSs).
