The privacy-preserving federated learning schemes based on the setting of two honest-but-curious and non-colluding servers offer promising solutions in terms of security and efficiency. However, our investigation reveals that these schemes still suffer from privacy leakage when considering model poisoning attacks from malicious users. Specifically, we demonstrate that the privacy-preserving computation process for defending against model poisoning attacks inadvertently leaks privacy to one of the honest-but-curious servers, enabling it to access users' gradients in plaintext. To address this issue, we propose an enhanced privacy-preserving and Byzantine-robust federated learning (PBFL) framework that simultaneously achieves privacy, robustness, and efficiency. Central to our design is a novel Byzantine-tolerant aggregation strategy that defends against both conventional and adaptive poisoning attacks. It integrates normalization judgment, cosine similarity computation, and adaptive user weighting, with a dual-scoring trust mechanism and outlier suppression for stealthy attacks. In addition, we develop two privacy-preserving subroutines, namely secure normalization judgment and secure cosine similarity measurement, which operate over encrypted gradients using a trapdoor fully homomorphic encryption (FHE) scheme, ensuring both confidentiality and robust aggregation correctness. Theoretical analyses confirm that our scheme guarantees security, convergence, and efficiency even with malicious users and one malicious server. Extensive experiments demonstrate that our method effectively breaks prior privacy attacks, maintains high accuracy under diverse poisoning strategies, and significantly reduces computation and communication overhead compared to state-of-the-art PBFL schemes.

The secure and privacy-preserving federated learning scheme, NSPFL, aims to safeguard data privacy while also auditing data integrity. The solution provided by this scheme is highly novel. However, NSPFL has significant design shortcomings in terms of both privacy protection and data integrity verification. This work identifies specific issues within NSPFL and proposes effective countermeasures. Furthermore, our proposed solution can serve as a general approach for privacy-preserving multiparty computations, safeguarding privacy while enhancing efficiency.

Data-driven Computer Vision (CV) tasks are still limited by the amount of labeled data. Recently, some semantic NeRFs have been proposed to render and synthesize novel-view semantic labels. Although current NeRF methods achieve spatially consistent color and semantic rendering, the capability of the geometrical representation is limited. This problem is caused by the lack of global information among rays in the traditional NeRFs since they are trained with independent directional rays. To address this problem, we introduce the point-to-surface global feature into NeRF to associate all rays, which enables the single ray representation capability of global geometry. In particular, the relative distance of each sampled ray point to the learned global surfaces is calculated to weight the geometry density and semantic-color feature. We also carefully design the semantic loss and back-propagation function to solve the problems of unbalanced samples and the disturbance of implicit semantic field to geometric field. The experiments validate the $3 D$ scene annotation capability with few feed labels. The quantification results show that our method outperforms the state-of-the-art works in efficiency, geometry, color and semantics on the public datasets. The proposed method is also applied to multiple tasks, such as indoor, outdoor, part segmentation labeling, texture re-rendering and robot simulation.

In this paper, we introduce a novel point-to-surface representation for 3D point cloud learning. Unlike the previous methods that mainly adopt voxel, mesh, or point coordinates, we propose to tackle this problem from a new perspective: learn a set of quadratic terms based static and global reference surfaces to describe 3D shapes, such that the coordinates of a 3D point (x, y, z) can be extended to quadratic terms (xy, xz, yz,$\ldots $) and transformed to the relationship between the local point and the global reference surfaces. Then, the static surfaces are changed into dynamic surfaces by adaptive contribution weighting to improve the descriptive capability. Towards this end, we propose our point-to-surface representation, a new representation for 3D point cloud learning that has not been attempted before, which can assemble local and global geometric information effectively by building connections between the point cloud and the learned reference surfaces. Given 3D points, we show how the reference surfaces are constructed, and how they are inserted into the 3D learning pipeline for different tasks. The experimental results confirm the effectiveness of our new representation, which has outperformed the state-of-the-art methods on the tasks of 3D classification and segmentation.

This paper proposes a novel 3D local descriptor, which seeks a good balance between the efficiency and the accuracy. We use the Local Reference Frame (LRF) to estimate a robust coordinate system to describe the local 3D shape. A novel Local Height Image (LHI) is defined by projecting the 3D points in the support region onto the tangent plane of the basis point. The Local Height Image Descriptor (LHID) is then defined by calculating the averaged projection distances. We further smooth the LHID to resist various kinds of interferences. We setup several experiments to assess the performance of our descriptor by comparison with the state-of-the-art algorithms. The experimental results demonstrate the effectiveness of the proposed method, which not only achieves the high accuracy as well as the robustness, but also possesses low complexity for the efficiency.

Filtered orthogonal frequency division multiplexing (F-OFDM) system has a high computational complexity due to its high-order filter. In this paper, a subband superposed OFDM (SS-OFDM) scheme, utilizing a multistage polyphase subfiltering structure, is proposed to reduce the complexity. In the SS-OFDM system, the entire transmission channel is divided into several subbands and each subband employs relatively low order filtering to depress its out-of-band emission (OOBE). Moreover, all the subbands implement parallel transmission in time domain, which decreases the operating rate, meanwhile shortening the filter length. Simulation results show that the spectral efficiency (with respect to spectrum occupancy ratio) is increased up to about 99% for the LTE, DTMB, and DVB-T standards, while preserving a low implementation complexity.