Heterogeneous Defect Prediction Based on Federated Reinforcement Learning via Gradient Clustering

Wang, Aili; Zhao, Yinghui; Li, Guodong; Zhang, Jun; Wu, Haibin; Iwahori, Yuji

doi:10.1109/access.2022.3195039

Cited by 9 publications

(3 citation statements)

References 45 publications

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“…Madi et al [41] proposed a secure framework relying on Homomorphic Encryption and Verifiable Computation, and conducted experiments on the FEMNIST dataset. Wang et al [42] proposed a federated reinforcement learning method based on gradient clustering (FRLGC). FRLGC adds Gaussian noise to the data of local clients to achieve data level privacy protection.…”

Section: B Federated Learningmentioning

confidence: 99%

Heterogeneous Defect Prediction Based on Federated Prototype Learning

Wang,

Yang,

et al. 2023

IEEE Access

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Software defect prediction is used to identify modules in software projects that may have defects. Heterogeneous Defect Prediction (HDP) establishes a cross project defect prediction model based on different software defect datasets. However, due to the heterogeneity of multi-source data, the model performance is usually not ideal. In addition, the project data holder is unwilling to disclose the data due to privacy regulations and other reasons, resulting in data islands. This paper presents a federal prototype learning based on prototype averaging (FPLPA), which combines federated learning (FL) with prototype learning for heterogeneous defect prediction. Firstly, the client used one-sided selection (OSS) algorithm to remove noise from local training data, and applied Chi-Squares Test algorithm to select the optimal subset of features. Secondly, the client constructed the convolution prototype network (CPN) to generate their own local prototypes. CPN are more robust to heterogeneous data than convolutional neural networks (CNN), while avoiding the deviation effect of class imbalances in software data. The prototype is used as the communication subject between the clients and the server. Because the local prototype is generated in an irreversible way, it can play a role of privacy protection in the communication process. Finally, the local CPN network is updated with the loss of local prototype and global prototype as regularization. We have verified on 10 projects in three public data sets (AEEEM, NASA and Relink), and the experimental results show that FPLPA is superior to other HDP solutions.INDEX TERMS Heterogeneous defect prediction, federated learning, prototype learning, data islands.

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Section: B Federated Learningmentioning

confidence: 99%

Heterogeneous Defect Prediction Based on Federated Prototype Learning

Wang,

Yang,

et al. 2023

IEEE Access

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“…The authors of [32] developed a flexible cluster federated learning framework that divides client clusters based on the similarity between edge model training directions and experimentally demonstrated that the model accuracy outperforms that of traditional FL systems. To address the interference of distributed heterogeneous environments on the prediction of defects, the authors in [33] proposed a FL framework based on model clustering by clustering encrypted model gradients to create suitable clusters and validated the framework with several open datasets. The results showed that the algorithm outperformed the related defect prediction algorithm.…”

Section: Related Workmentioning

confidence: 99%

High-Precision Cluster Federated Learning for Smart Home: An Edge-Cloud Collaboration Approach

Li,

Yang,

Sun

et al. 2023

IEEE Access

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Owing to the strong protection of data privacy, federated learning (FL) has become a key method for achieving intelligent decision making in smart homes. However, under realistic conditions, such as differentiated requirements and heterogeneous service environments, FL in smart homes faces the problem of non-independent and identically distributed (non-IID) data and uneven computing power, which leads to the poor adaptability of global models. To address this issue, this study proposes a cluster FL architecture based on edge-cloud collaboration. First, a Gaussian mixture model-based cluster FL is proposed to improve the model accuracy by clustering features on the FL dataset and ensuring an independent identical distribution of the data. Subsequently, a model training strategy based on edge-cloud collaboration is proposed to achieve the sharing of edge-cloud computing power by split learning, which provides sufficient computing power for model training. The simulation results show that the proposed architecture improves the accuracy of global models while ensuring normal network service provision.INDEX TERMS federated learning, edge-cloud collaboration, smart home

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“…Experiments on the open-source software defect prediction dataset showed that this method improves the performance of the model, greatly reduced the impact of low-quality data on the model, and enhanced the robustness of the model. Wang et al proposed federated reinforcement learning via gradient clustering (FRLGC) [12] and verified that FRLGC is superior to the related cross-project software defect prediction methods in three public databases.…”

Section: Introductionmentioning

confidence: 99%

Efficient Cross-Project Software Defect Prediction Based on Federated Meta-Learning

Chen,

Yang,

Wang

2024

Electronics

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Software defect prediction is an important part of software development, which aims to use existing historical data to predict future software defects. Focusing on the model performance and communication efficiency of cross-project software defect prediction, this paper proposes an efficient communication-based federated meta-learning (ECFML) algorithm. The lightweight MobileViT network is used as the meta-learner of the Model Agnostic Meta-Learning (MAML) algorithm. By learning common knowledge on the local data of multiple clients, and then fine-tuning the model, the number of unnecessary iterations is reduced, and communication efficiency is improved while reducing the number of parameters. The gradient information model is encrypted using the differential privacy of the Laplace mechanism, and the optimal privacy budget is determined through experiments. Experiments on three public datasets (AEEEM, NASA, and Relink) verified the effectiveness of ECFML in terms of parameter quantity, convergence, and model performance of cross-project software defect prediction.

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Heterogeneous Defect Prediction Based on Federated Reinforcement Learning via Gradient Clustering

Cited by 9 publications

References 45 publications

Heterogeneous Defect Prediction Based on Federated Prototype Learning

Heterogeneous Defect Prediction Based on Federated Prototype Learning

High-Precision Cluster Federated Learning for Smart Home: An Edge-Cloud Collaboration Approach

Efficient Cross-Project Software Defect Prediction Based on Federated Meta-Learning

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