4 accepted papers
Federated Learning (FL) enables collaborative training of a global model without data sharing, yet it faces critical challenges from privacy leakage and Byzantine attacks. Existing privacy-preserving robust FL frameworks suffer from three key limitations: high computational costs, restricted applica…
Federated learning (FL) is a promising approach for privacy-preserving machine learning, enabling collaborative model training across distributed devices without sharing raw data. However, FL faces significant challenges due to the nonindependent and identically distributed (non-IID) nature of data…
The intensified concerns arising from the widespread adoption of deep learning have led to increased scrutiny of intellectual property protection in DNN models. Existing audio watermarking techniques, predominantly based on traditional signal processing methods, struggle to balance robustness, imper…
With the rapid proliferation of social networks, individuals now have greater access to news with increased speed. Simultaneously, there has been a heightened emphasis on detecting and mitigating the dissemination of fake news. One notable limitation of existing fake news detection models is their i…