Federated Learning (FL) has recently gained prominence in the context of Vehicular Ad-hoc Networks (VANETs) as a promising approach to enhancing autonomous driving capabilities. However, vehicles' high mobility, real-time communication, and dynamic network topology lead to frequent disconnections during operation, which may slow down the convergence of FL or even lead to training failure. In this study, we propose a scalable aggregation scheme (SAPE) designed to improve computation efficiency and address vehicle dropouts. SAPE employs a lossless encoding algorithm with parallel technology for efficient aggregation of large vectors. Then, we leverage TJL (ACSAC '22) to reconstruct gradients for dropout vehicles, using online vehicles to establish a$k$-regular graph. In a network with$N$vehicles, SAPE achieves a secure aggregation overhead of$O(log^{2}(N))$, as opposed to$O(N^{2})$, tolerating a vehicle dropout rate of up to 33%. Furthermore, we conduct a theoretical security analysis of SAPE to prove its security under honest-but-curious (HBC) and malicious attack models. Extensive experiments show that SAPE outperforms existing baseline aggregation schemes by up to 1.4× speedups in aggregation time.

Video-based speech-visual emotion recognition plays a crucial role in human-computer interaction applications. However, it faces several challenges, including: 1) the redundancy in the extracted speech-visual features caused by the heterogeneity between speech and visual modalities, and 2) the ineffective modeling of the time-varying characteristics of emotions. To this end, this paper proposes an adaptive alignment and time aggregation network (AataNet). Specifically, AataNet designs a low redundancy speech-visual adaptive alignment (LRSVAA) module to acquire the low-redundant aligned features of speech-visual modalities. Meanwhile, AataNet also designs a computationally efficient time-adaptive aggregation (CETAA) module to model the time-varying characteristics of emotions. Experiments on RAVDESS, BAUM-1 s and eNTERFACE05 datasets also demonstrate that the proposed AataNet achieves better results.

The advent of equipment condition evaluation at the edge, facilitated by the Internet of Things (IoT), has led to significant reduction in data transmission and improvement in diagnostic efficiency. Nevertheless, the multi-scale components and noise interferences will seriously affect the quality of end-side sensed signals. Additionally, the mismatch between edge-side rigid models and time-varying nature of data features can lead to the failure of edge evaluation. To overcome these issues, this study introduces an IoT-based adaptive multiplication-convolution sparse denoising (AMCSD) method for the accurate equipment edge condition evaluation. Initially, from the perspective of signal processing synergized with deep learning, a lightweight signal denoising network is proposed with an array of multiplication filtering kernels (MFKs). Guiding by fault signal modulation mechanism, the learned MFKs sparse filters can adaptively extracted the fault-related frequency features with irrelevant components suppressed from the time-series differential information distributed in the degeneration process. Subsequently, an end-edge collaborative mechanism framework is designed and deployed on end-edge hardware unit. A compact end-side processing node (EPN) prototype can achieve efficient edge denoising effect with data adaptive compressing. Concurrently, the edge-side device named AlxBoard can implement the model adaptive dynamic updating. This means that adaptive signal denoising employed in the end-side focuses on the improvement of signal quality and sparse filter learning encompassed in the edge-side aims to solve the model mismatch. These advancements are anticipated to provide a monotonic but sensitive evaluation of degradation at the edge, surpassing the capability of conventional approaches.

Federated learning (FL) is vulnerable to data poisoning attacks when an adversary attempts to upload poison gradients with the intent to corrupt the global model of FL. Various approaches have been proposed to counter these risks. However, it becomes challenging when one tries to preserve the privacy of FL participants and ensure robustness against data poisoning attacks. In this paper, we propose DPFLA, a novel scheme that can detect poisoning attacks without revealing the actual gradients of participants. DPFLA is a lossless aggregation scheme delicately designed for adopting masks to protect private data while extracting poisoned data features. Specifically, we first apply removable masks to the gradients outputted by each participant. Second, we aggregate the masked data and decompose them using Singular Value Decomposition (SVD) to extract specific features as well as achieve dimensionality reduction. Third, we leverage a clustering paradigm to detect poison gradients from the low dimension and eliminate them in the following training rounds. We conducted extensive experiments to demonstrate that DPFLA can detect poison gradients effectively. Additionally, the comparisons of case studies demonstrate that DPFLA outperforms the state-of-the-art methods.