The rapid growth of cloud computing has enabled collaborative data analysis across distributed systems, yet ensuring privacy under stringent regulations remains a critical challenge. Data Collaboration Analysis (DCA) facilitates efficient analysis in such environments but struggles to balance robust privacy with model accuracy under strict constraints . Traditional Differential Privacy (DP) methods often degrade performance due to excessive noise (e.g.,$\epsilon = 2$). We propose SSDP-DCA, a novel framework that enhances DCA by integrating DP with privacy amplification techniques, leveraging the Shuffle Model for anonymization andpackage Subsampling (applied post-shuffle)for privacy amplification. Experimental results demonstrate SSDP-DCA's superiority over baselines, achieving high utility under a fixed global privacy budget, thus offering a robust solution for secure federated learning and healthcare applications.

Due to its ease of deployment and high mobility, unmanned aerial vehicles (UAVs) have gained great popularity for a variety of applications. To conduct high-level and complicated tasks such as search/rescue missions and target identification, deep learning functions at UAVs are required. To this end, distributed learning methods such as federated learning (FL) and split learning (SL) have been proposed. In this paper, we investigate the SL assisted image classification task in a multi-UAV system for applications such as area exploration and object detection. Specifically, the whole deep learning model is cut into the UAV-side model and BS (base station)-side model. Each UAV performs forward propagation on UAV-side model by using the locally gathered images, and sends the smashed data to the BS. The BS performs forward and backward propagation based on the smashed data, and sends back the gradients of the cut layer to the UAVs, which is used for the backward propagation of the UAV-side model. The performance was evaluated using an aerial perspective geographic dataset, and the effectiveness of the proposed system was validated by comparing with FL-based and centralized learning methods. It was found that SL can significantly reduce computation time at UAV compared with FL, and is particularly effective with non-IID (independent and identically distributed) dataset. SL also requires less data during the training initial phase and has a faster convergence speed compared to centralized learning.

Localizing sensors based on Received Signal Strength Indicator (RSSI) localization technique in wireless sensor network can be treated as building a mapping between signal and physical spaces, and the mapping is established from a set of given paired signal strengths and physical location data of known sensors. However, in some realistic scenarios, such a set of completely-paired sensor data is not always accessible, which brings a big challenge for localization of sensors. The localization research in such a scenario is currently almost ignored. In this paper, we develop a novel algorithm to tackle this problem in localization with paired as well as many unpaired data by adapting our previously-proposed Locality Correlation Analysis model; the new algorithm is named as Partially Paired Locality Correlation Analysis (PPLCA). Experimental results in both outdoor and indoor environments do show the feasibility and effectiveness of the proposed algorithm.

Multimodal recognition is an emerging technique to overcome the non-robustness of the unimodal recognition in real applications. Canonical correlation analysis (CCA) has been employed as a powerful tool for feature fusion in the realization of such multimodal system. However, CCA is the unsupervised feature extraction and it does not utilize the class information of the samples, resulting in the constraint of the recognition performance. In this paper, the class information is incorporated into the framework of CCA for combined feature extraction, and a novel method of combined feature extraction for multimodal recognition, called discriminative canonical correlation analysis (DCCA), is proposed. The experiments show that DCCA outperforms some related methods of both unimodal recognition and multimodal recognition.

In this paper, we develop a new effective multiple kernel learning algorithm. First, map the input data into m different feature spaces by m empirical kernels, where each generatedfeature space is takenas one viewof the input space. Then through the borrowing the motivating argument from Canonical Correlation Analysis (CCA)that can maximally correlate the m views in the transformed coordinates, we introduce a special term called Inter-Function Similarity Loss R IFSL into the existing regularization framework so as to guarantee the agreement of multi-view outputs. In implementation, we select the Modification of Ho-Kashyap algorithm with Squared approximation of the misclassification errors (MHKS) as the incorporated paradigm, and the experimental results on benchmark data sets demonstrate the feasibility and effectiveness of the proposed algorithm named MultiK-MHKS.