Federated Learning (FL) has attracted extraordinary attention from the industry and academia due to its advantages in privacy protection and collaboratively training on isolated datasets. Since machine learning algorithms usually try to find an optimal hypothesis to fit the training data, attackers also can exploit the shared models and reversely analyze users’ private information. However, there is still no good solution to solve the privacy-accuracy trade-off, by making information leakage more difficult and meanwhile can guarantee the convergence of learning. In this work, we propose a Loss Differential Strategy (LDS) for parameter replacement in FL. The key idea of our strategy is to maintain the performance of the Private Model to be preserved through parameter replacement with multi-user participation, while the efficiency of privacy attacks on the model can be significantly reduced. To evaluate the proposed method, we have conducted comprehensive experiments on four typical machine learning datasets to defend against membership inference attack. For example, the accuracy on MNIST is near 99%, while it can reduce the accuracy of attack by 10.1% compared with FedAvg. Compared with other traditional privacy protection mechanisms, our method also outperforms them in terms of accuracy and privacy preserving.

Due to limited transmission resources and storage capacity, efficient rate control is important in Video-based Point Cloud Compression (V-PCC). In this paper, we propose a learning-based rate control method to improve the rate-distortion (RD) performance of V-PCC. A low-latency synchronous rate control structure is designed to reduce the overhead of pre-coding. The basic unit (BU) parameters are predicted accurately based on our proposed CNN-LSTM neural network, instead of the online updating approach, which can be inaccurate due to low consistency between adjacent 2D frames in V-PCC. When determining the quantization parameters for the BU, a patch-based clipping method is proposed to avoid unnecessary clipping. This approach is able to improve the RD performance and subjective dynamic point cloud quality. Experiments show that our proposed rate control method outperforms present approaches.

Intra/inter switching-based error resilient video coding effectively enhances the robustness of video streaming when transmitting over error-prone networks. But it has a high computation complexity, due to the detailed end-to-end distortion prediction and brute-force search for rate-distortion optimization. In this article, a Low Complexity Mode Switching based Error Resilient Encoding (LC-MSERE) method is proposed to reduce the complexity of the encoder through a deep learning approach. By designing and training multi-scale information fusion-based convolutional neural networks (CNN), intra and inter mode coding unit (CU) partitions can be predicted by the networks rapidly and accurately, instead of using brute-force search and a large number of end-to-end distortion estimations. In the intra CU partition prediction, we propose a spatial multi-scale information fusion based CNN (SMIF-Intra). In this network a shortcut convolution architecture is designed to learn the multi-scale and multi-grained image information, which is correlated with the CU partition. In the inter CU partition, we propose a spatial-temporal multi-scale information fusion-based CNN (STMIF-Inter), in which a two-stream convolution architecture is designed to learn the spatial-temporal image texture and the distortion propagation among frames. With information from the image, and coding and transmission parameters, the networks are able to accurately predict CU partitions for both intra and inter coding tree units (CTUs). Experiments show that our approach significantly reduces computation time for error resilient video encoding with acceptable quality decrement.