9 papers
Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
Shiyuan Zuo, Jiashuo Li, Rongfei Fan +2
Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks. Existing robust approac…
Dual-View Predictive Diffusion: Lightweight Speech Enhancement via Spectrogram-Image Synergy
Ke Xue, Rongfei Fan, Kai Li +3
Diffusion models have recently set new benchmarks in Speech Enhancement (SE). However, most existing score-based models treat speech spectrograms merely as generic 2D images, apply…
Omni-directional attention mechanism based on Mamba for speech separation
Ke Xue, Chang Sun, Rongfei Fan +2
Mamba, a selective state-space model (SSM), has emerged as an efficient alternative to Transformers for speech modeling, enabling long-sequence processing with linear complexity. W…
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
Shiyuan Zuo, Xingrun Yan, Rongfei Fan +4
Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but is vulnerable to Byzantine attacks and data heterogeneity, which can…
From Coarse to Fine: Recursive Audio-Visual Semantic Enhancement for Speech Separation
Ke Xue, Rongfei Fan, Lixin +3
Audio-visual speech separation aims to isolate each speaker's clean voice from mixtures by leveraging visual cues such as lip movements and facial features. While visual informatio…
H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning
Shiyuan Zuo, Rongfei Fan, Cheng Zhan +3
Federated Learning (FL) enables decentralized model training without sharing raw data. However, it remains vulnerable to Byzantine attacks, which can compromise the aggregation of…