Current research shows that the privacy of FL is threatened by an honest-but-curious server. However, existing research focus on privacy attacks against the malicious server while overlooking that it could also compro...
Current research shows that the privacy of FL is threatened by an honest-but-curious server. However, existing research focus on privacy attacks against the malicious server while overlooking that it could also compromise the shared model's integrity by introducing poisoning attacks. In this work, we propose a novel data-free backdoor attack (DaBA) against FL via malicious server to bridge the gap. Specifically, we utilize global model inversion to obtain a dummy dataset on the server side, then add backdoor triggers to a portion of the inputs in the dummy dataset and replace their labels with the target label, and finally retrain part of the global model on the poisoned dummy dataset. Our experimental results show that DaBA can achieve a high attack success rate on poisoned samples and high prediction accuracy on clean samples, which means the effectiveness and stealthiness of DaBA, respectively. For example, in the experiment of the MNIST dataset, DaBA can achieve a 99.6% attack success rate and 96.3% accuracy rate. We also discuss possible defense strategies against our attack. Our research reveals a significant security risk of FL.
In off-policy reinforcement learning, prioritized experience replay plays an important role. However, the centralized prioritized experience replay becomes the bottleneck for efficient training. We propose to approxim...
详细信息
Sparse matrix reordering is an important step in Cholesky decomposition. By reordering the rows and columns of the matrix, the time of computation and storage cost can be greatly reduced. With the proposal of various ...
详细信息
ISBN:
(数字)9798350359312
ISBN:
(纸本)9798350359329
Sparse matrix reordering is an important step in Cholesky decomposition. By reordering the rows and columns of the matrix, the time of computation and storage cost can be greatly reduced. With the proposal of various reordering algorithms, the selection of suitable reordering methods for various matrices has become an important research topic. In this paper, we propose a method to predict the optimal reordering method by visualizing sparse matrices in chunks in a parallel manner and feeding them into a deep convolutional neural network. The results show that the theoretical performance can reach 95% of the optimal performance, the prediction accuracy of the method can reach up to 85%, the parallel framework achieves an average speedup ratio of 11.35 times over the serial framework, and the performance is greatly improved compared with the traversal selection method on large sparse matrices.
Cold data contributes a large portion of the big data today and is usually stored in secondary storage. Various sketch data structures are implemented to represent the stored elements and provide constant-time members...
详细信息
While most existing speech-driven talking head generation methods provide effective solutions, they primarily focus on the facial area. However, producing upper-body talking videos from speech remains challenging. Add...
详细信息
ISBN:
(纸本)9798400718779
While most existing speech-driven talking head generation methods provide effective solutions, they primarily focus on the facial area. However, producing upper-body talking videos from speech remains challenging. Addressing how to use speech to simultaneously drive subtle facial motion and large-scale body motion while generating naturally synchronized upper body video frames is urgent. In this study, we propose AnchorTalk, a novel system based on tri-plane hash NeRF, capable of producing high-quality anchor-style talking videos. Firstly, to integrate both rigid and non-rigid motion within a unified system, we introduce a coarse-to-fine framework that consists of coarse pose generation and facial details optimization. A speech disentanglement encoder decouples speech features into pose-related and head-related features to drive the motion of the body and head. Secondly, during the coarse pose generation phase, we propose a geometry correction module to obtain precise body parameters to guide the body motion. Thirdly, less detailed head parameters can lead to facial distortion and disjointed motion during facial optimization. To mitigate this issue, we propose a head controller to capture facial expressions accurately. By fine-tuning the model on a one-minute video, the system can generalize to novel identities. Experimental results validate the effectiveness and feasibility of our method in generating high-quality, coherent upper-body talking human videos from speech.
Accurately mapping the surface rivers is important in ecological environment monitoring and disaster prevention. The development of remote sensing technology and computer vision greatly improves the efficiency of this...
详细信息
Deep Reinforcement Learning has been successfully applied in various applications and achieved impressive performance compared with previous traditional methods but suffers from high computation cost and long training...
详细信息
Deep Reinforcement Learning has been successfully applied in various applications and achieved impressive performance compared with previous traditional methods but suffers from high computation cost and long training time. MLPerf takes deep reinforcement learning as one of the benchmark tracks and provides a single node training version of MiniGo as a reference. A key challenge is to achieve efficient MiniGo training on a large-scale computing system. According to the training computation pattern in MiniGo and the characteristics of our large-scale heterogeneous computing system, we propose a MultiLevel parallel strategy, MLPs, including task-level parallelism between nodes, CPU-DSP heterogeneous parallelism, and DSP multi-core parallelism. The proposed method reduces the overall execution time from 43 hours to 16 hours while scaling the node size from 1067 to 4139. The scaling efficiency is 69.1%. According to our fitting method, the scaling efficiency is 46.5% when scaling to 8235 nodes. The experimental results show that the proposed method achieves the efficient training of MiniGo on the largescale heterogeneous computing system.
With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreove...
详细信息
Disaggregated memory (DM) is a widely discussed datacenter architecture in academia and industry. It decouples computing and memory resources from monolithic servers into two network-connected resource pools. Range in...
详细信息
In the Internet of Everything (IoE), due to its issues of complexity and heterogeneity, message delay cannot be guaranteed, and it is not enough to leverage a centralized model for data collaboration. By leveraging th...
详细信息
暂无评论