In this paper, we propose a novel federated split learning framework, FedSL, to train models on distributed sequential data. The most common ML models to train on sequential data are Recurrent Neural Networks (RNNs). Since the proposed framework is privacy preserving, segme...
Privacy preserving distributed learning classifiers—Sequential learning with small sets of data. Comput. Biol. Med. 2021, 136, 104716. [Google Scholar] [CrossRef] [PubMed] Sun, G.; Cong, Y.; Dong, J.; Wang, Q.; Lyu, L.; Liu, J. Data Poisoning Attacks on Federated Machine Learning...
ALADIN iterates between the parallel optimization of subproblems and a sequential quadratic programming (SQP) step for the coordination. This has been applied to nonconvex model predictive control and optimal power flow (Engelmann et al., 2020), as well as sensor localization problems (Houska et ...
轮次被揭示,该过程被称为一种新的序列组合多臂赌博机(sequentialcombinatorial multi-armedbandit,SCMAB)问题。决策智能体的目标是根据反馈的完成时间来指导选 择最佳的决策的序列,最大化其在所有轮次上的总期望收益。为了有效的求解SCMAB问 题,本白皮书提出基于NaiveUCB算法在每一轮制定一个决策的序列,以一种在线的...
In fact, a cover with a minimal number of intervals is found by the algorithm in [29] (in \(O(n\log n)\) sequential time). The covers produced by [29] are sufficient for that matter, as the following lemma states.Lemma 4.7
We propose an efficient method using multi-objective genetic algorithm (MOGAMOD) to discover optimal motifs in sequential data. The main advantage of our a... M Kaya - 《Expert Systems with Applications》 被引量: 117发表: 2009年 Multiperiodicity analysis and numerical simulation of discrete-time...
Privacy preserving distributed learning classifiers – Sequential learning with small sets of data 2021, Computers in Biology and Medicine Citation Excerpt : Since the performance and the robustness of an AI model is directly related to the number of samples on which it was trained and validated [...
Stochastic gradient descent is a widely used method to find locally-optimal models in machine learning and data mining. However, it is naturally a sequential algorithm, and parallelization involves severe compromises because the cost of ... H Zhao,JF Canny,H Zhao,... 被引量: 4发表: 2012年 ...
kidney, and prostate. To simulate time, each dataset is exclusively accessed for a defined period of time during the learning process, starting from the liver dataset. Thus, there are four sequential continual learning tasks that use the liver, breast, kidney, and prostate datasets, respectively....
This has determined their widespread adoption in modeling sequential data for the prediction of occupancy in environmental monitoring [26]. In addition, people are afraid of solutions that physically wire them to sensors, and thus, they require device-free technologies for their monitoring [27]. So...