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

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

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' ...
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

FedASA: A Personalized Federated Learning With Adaptive Model Aggregation for Heterogeneous Mobile Edge Computing

Abstract: Federated learning (FL) opens a new promising paradigm for the Industrial Internet of Things (IoT) since it can collaboratively train machine learning models without sharing private data.