Point cloud processing has become a cornerstone technology in many 3D vision tasks. However, arbitrary rotations introduce variations in point cloud orientations, posing a long-standing challenge for effective representation learning. The core of this issue is the disruption of the point cloud's intrinsic directional characteristics caused by rotational perturbations. Recent methods attempt to implicitly model rotational equivariance and invariance, preserving directional information and propagating it into deep semantic spaces. Yet, they often fall short of fully exploiting the multiscale directional nature of point clouds to enhance feature representations. To address this, we propose the Direction-Perceptive Vector Network (DiPVNet). At its core is an atomic dot-product operator that simultaneously encodes directional selectivity and rotation invariance--endowing the network with both rotational symmetry modeling and adaptive directional perception. At the local level, we introduce a Learnable Local Dot-Product (L2DP) Operator, which enables interactions between a center point and its neighbors to adaptively capture the non-uniform local structures of point clouds. At the global level, we leverage generalized harmonic analysis to prove that the dot-product between point clouds and spherical sampling vectors is equivalent to a direction-aware spherical Fourier transform (DASFT). This leads to the construction of a global directional response spectrum for modeling holistic directional structures. We rigorously prove the rotation invariance of both operators. Extensive experiments on challenging scenarios involving noise and large-angle rotations demonstrate that DiPVNet achieves state-of-the-art performance on point cloud classification and segmentation tasks.

Despite its success in many applications, federated learning is increasingly vulnerable to sophisticated poisoning attacks. Existing defenses, particularly Byzantine Robust Aggregation Rules (BRARs), offer some protection but rely on strong assumptions or challenging technical prerequisites. To address these shortcomings, we propose anensemble defense with provably convergent aggregation(EndPCA). By using the entropy weight method to consolidate scores from multiple BRARs into an ensemble trust score, it effectively integrates heterogeneous weak BRARs to resist a wide range of poisoning attacks under practical assumptions. We formally prove that EndPCA can provide theoretical guarantees of convergence with bounded error. Our empirical evaluations show that EndPCA consistently outperforms existing BRARs, demonstrating its effectiveness across various scenarios.

Abstract Federated Learning (FL) enables multiple clients to collaboratively train a shared model without exposing local data, making it a fundamental paradigm for large-scale distributed intelligence. However, in practical edge-cloud deployments, the server must inspect a large volume of high-dimensional client updates within tight communication windows, which makes secure aggregation a problem closely tied to parallel processing, real-time response, and high-performance computing (HPC) resources. Among the major threats to FL, backdoor attacks are particularly insidious because they implant malicious behaviors into the global model while preserving benign-task performance. Although pre-aggregation defenses based on gradient analysis are promising, the current state-of-the-art methods such as Scope suffer from two key limitations: fixed parameters are ineffective against adaptive attackers, and single-point clustering is vulnerable to failure under heterogeneous (non-IID) data distributions. To address these limitations, we propose FAROS, a robust and HPC-friendly defense framework that generalizes the transform-and-cluster paradigm. FAROS incorporates two key components: Adaptive Differential Scaling (ADS), which dynamically adjusts defense sensitivity according to the dispersion of client gradients, and Robust Core-set Computing (RCC), which replaces single-point clustering with a consensus-based centroid derived from a stable core-set. This design improves robustness while preserving server-side efficiency through vectorizable similarity computation and parallelizable filtering. Extensive experiments on multiple datasets, models, and attack settings show that FAROS consistently outperforms existing defenses in both attack suppression and benign-task accuracy, while remaining compatible with scalable distributed FL infrastructures.

As mobile and edge devices become more prevalent, applications handling sensitive data, such as health monitoring, age verification for online services , and vulnerability scanning tools , are becoming increasingly common. The comparison of critical data (e.g., health metrics, ages, or software versions) is essential for informed decision-making. However, securely processing this sensitive information in resource-limited environments presents significant challenges. To address these challenges, we propose a server-aided secure interval test and comparison protocol based on point and range encoding. The proposed protocol allows two parties to verify if a value belongs to an interval or is less than another value without revealing their private inputs. To handle the set membership problem in the encoding, we introduce an innovative oblivious updatable key pseudorandom function. Leveraging this function, our protocol achieves efficient delegation to a cloud server, ensures that online costs grow linearly with input length, and maintains exceptionally low amortized preprocessing costs. We formally prove the semi-honest security of our protocol and compare it to the other semi-honest protocols. On the client side, our protocol requires only tens of microseconds for computational time and less than 1 KB for communication cost for inputs up to 40 bits. Compared to the other secure interval test protocols, it significantly reduces both computational and communication costs, especially for larger inputs. Moreover, it achieves the lowest computation costs while maintaining similar communication costs to the other secure comparison protocols.