IEEE

SHIELD - Secure Aggregation Against Poisoning in Hierarchical Federated Learning

Abstract: Federated Learning (FL) is a privacy-preserving distributed Machine Learning (ML) technique. Hierarchical FL is a novel variant of FL applicable to networks with multiple layers. Instead of ...
IEEE

MNAT-Net: Multi-Scale Neighborhood Aggregation Transformer Network for Point Cloud Classification and Segmentation

Abstract: Accurate understanding of 3D objects in complex scenes plays essential roles in the fields of intelligent transportation and autonomous driving technology. Recent deep neural networks have ...
IEEE

Optimizing Federated Learning With Aggregation Strategies: A Comprehensive Survey

Abstract: This article provides a comprehensive survey of aggregation strategies in federated learning (FL). This decentralized machine learning (ML) paradigm enables multiple clients to ...
IEEE

FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning

Abstract: Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is ...
IEEE

WheatNet: Attentional Path Aggregation Feature Pyramid Network for Precise Detection and Counting of Dense and Arbitrary-Oriented Wheat Spikes

Abstract: Achieving the precise and real-time detection of wheat spikes play a crucial role in wheat growth monitoring for precision agriculture community. Machine-learning methods are commonly ...
IEEE

Point Cloud Registration via Reconstruction with Local Geometry Information Aggregation

Abstract: Point cloud registration is a fundamental yet challenging task in computer vision and robotics. While framing it as a reconstruction problem has shown promise, traditional reconstruction ...
IEEE

Towards Privacy-Preserving and Error-Tolerant Wireless Federated Learning: Fixed-Point Model Aggregation with Differential Privacy Guarantees

Abstract: This paper presents a novel approach for wireless federated learning (WFL) that, for the first time, enables the aggregation of local models with mild to moderate errors under practical ...
IEEE

CANet: Cellular Aggregation Network for Point Cloud Quality Assessment

Abstract: The concept of visual masking reveals that human visual perception is influenced by content and distortion information. Existing projection-based methods lose depth information and intrinsic ...
IEEE

Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning

Abstract: In Federated Learning (FL), the issue of statistical data heterogeneity has been a significant challenge to the field's ongoing development. This problem is further exacerbated when clients' ...