FedDSR: Daily Schedule Recommendation in a Federated Deep Reinforcement Learning Framework

Huang, Wei; Liu, Jia; Li, Tianrui; Huang, Tianqiang; Ji, Shenggong; Wan, Jihong

doi:10.1109/tkde.2021.3130265

Cited by 19 publications

(3 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to address the local clients' data distributions diverge, Sattler et al group the client population into clusters which have jointly trainable data distribution by using the geometric properties of the FL loss surface [26]. Huang et al proposed a federated learning framework (FedDSR) and a similarity aggregation algorithm to improve the quality of the model [27].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Defect Prediction Based on Federated Reinforcement Learning via Gradient Clustering

Wang

Zhao

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Heterogeneous defect prediction (HDP) refers to using heterogeneous data collected by other projects to build a defect prediction model to predict the software modules prone to defects in a project. Traditional methods usually involve the measurement of the source project and the target project. However, due to the limitations of laws and regulations, these original data are generally not easy to obtain, which forms a data island. As a new machine learning paradigm, federated learning (FL) has great advantages in training heterogeneous and data island. In order to solve the data island and data heterogeneity of HDP, we propose a novel Federated Reinforcement Learning via Gradient Clustering (FRLGC) method in this paper. Firstly, the parameters of the global model are transferred to each dueling deep Q network (dueling DQN) model and each client uses private data to train the dueling model which combines experience replay to increase data efficiency in limited datasets. Secondly, gaussian differential privacy is used to encrypt the model parameters to ensure the privacy and security of the model. Finally, we cluster the clients according to their locally encrypted model parameters and use weighted average to aggregate to create a new global model locally and globally. Experiments on nine projects in three public databases (Relink, NASA and AEEEM) show that FRLGC is superior to the relevant HDP solutions.INDEX TERMS data island, federated reinforcement learning, experience replay, Gaussian differential privacy.

show abstract

Section: Introductionmentioning

confidence: 99%

Heterogeneous Defect Prediction Based on Federated Reinforcement Learning via Gradient Clustering

Wang

Zhao

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In this case, we noticed Automatic Curriculum Learning (ACL) [13]- [16], which is a learning method that can select samples with the difficulty of samples. The ACL can be roughly divided into three categories, including Self-paced learning [17], [18], Transfer Teacher [19], [20], and Reinforcement learning teacher [21], [22].…”

Section: Introductionmentioning

confidence: 99%

“…The training of the feature encoder is based on self-supervised comparative learning with the optimal transport distance, which forces the similar tasks to gather while the unsimilar tasks scatter. Then, the OTTS can select the tasks before or during the training of classifier, which is similar to the teacher model of curriculum learning [13]- [16]. In this paper, we conduct a property experiment to verify the correctness of the training process and the distance of tasks.…”

Section: Introductionmentioning

confidence: 99%

Selecting task with optimal transport self-supervised learning for few-shot classification

Xu¹,

Yang²,

Liu³

et al. 2022

Preprint

View full text Add to dashboard Cite

Few-Shot classification aims at solving problems that only a few samples are available in the training process. Due to the lack of samples, researchers generally employ a set of training tasks from other domains to assist the target task, where the distribution between assistant tasks and the target task is usually different. To reduce the distribution gap, several lines of methods have been proposed, such as data augmentation and domain alignment. However, one common drawback of these algorithms is that they ignore the similarity task selection before training. The fundamental problem is to push the auxiliary tasks close to the target task. In this paper, we propose a novel task selecting algorithm, named Optimal Transport Task Selecting (OTTS), to construct a training set by selecting similar tasks for Few-Shot learning. Specifically, the OTTS measures the task similarity by calculating the optimal transport distance and completes the model training via a self-supervised strategy. By utilizing the selected tasks with OTTS, the training process of Few-Shot learning become more stable and effective. Other proposed methods including data augmentation and domain alignment can be used in the meantime with OTTS. We conduct extensive experiments on a variety of datasets, including MiniImageNet, CIFAR, CUB, Cars, and Places, to evaluate the effectiveness of OTTS. Experimental results validate that our OTTS outperforms the typical baselines, i.e., MAML, matchingnet, protonet, by a large margin (averagely 1.72% accuracy improvement).

show abstract

Exploring the evolution, progress, and future of point-of-interest recommendation over location-based social network: a comprehensive review

Acharya,

Mohbey

2024

Geoinformatica

View full text Add to dashboard Cite

FedDSR: Daily Schedule Recommendation in a Federated Deep Reinforcement Learning Framework

Cited by 19 publications

References 49 publications

Heterogeneous Defect Prediction Based on Federated Reinforcement Learning via Gradient Clustering

Heterogeneous Defect Prediction Based on Federated Reinforcement Learning via Gradient Clustering

Selecting task with optimal transport self-supervised learning for few-shot classification

Exploring the evolution, progress, and future of point-of-interest recommendation over location-based social network: a comprehensive review

Contact Info

Product

Resources

About