Heterogeneous defect prediction (HDP) refers to using heterogeneous data collected by other projects to build a defect prediction model to predict the software 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 not easy to obtain, which forms a data island. As a new machine learning paradigm, federated learning (FL) has great advantages in training heterogeneous data 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.

Automatic modulation recognition (AMR) has been wildly used in both military and civilian fields. Since the recognition of digital signal at low signal-to-noise (SNR) ratio is difficult and complex, in this paper, a clustering analysis algorithm is proposed for its recognition. Firstly, the digital signal constellation is extracted from the received waveform (digital signal + noise) by using the orthogonal decomposition and then, it is denoised by using an algorithm referred to as auto density-based spatial clustering technique in noise (ADBSCAN). The combination of density peak clustering (DPC) algorithm and improved K-means clustering is used to extract the constellation’s graph features, the eigenvalues are input into cascade support vector machine (SVM) multi-classifiers, and the signal modulation mode is obtained. BPSK, QPSK, 8PSK, 16QAM and 32QAM five kinds of digital signals are trained and classified by our proposed method. Compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR (less than 4dB), which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.