FLARE: A Wireless Side-Channel Fingerprinting Attack on Federated Learning

The introduction of SpectralKrum presents a novel defense mechanism against Byzantine attacks in Federated Learning (FL), addressing vulnerabilities where malicious clients can disrupt the training process by submitting corrupted updates. This method combines spectral subspace estimation with geometric neighbor-based selection to enhance the robustness of model training across heterogeneous client data distributions.

A recent study has introduced a weakly supervised approach for localizing tuberculosis (TB) in chest X-rays using knowledge distillation techniques, specifically employing a teacher-student framework with ResNet50 architecture. This method aims to reduce reliance on spurious correlations and eliminate the need for extensive bounding-box annotations, which are often resource-intensive to obtain.

A novel module-wise federated learning framework has been proposed to enhance grasp pose detection (GPD) in cluttered environments, addressing the challenges of data privacy and communication overhead associated with large models. This framework identifies slower-converging modules and allocates additional communication resources during training, thereby improving efficiency for resource-constrained robots.

A recent study has introduced a Federated Learning (FL) framework aimed at identifying at-risk students while ensuring data privacy. Utilizing the OULAD dataset, the research compares model complexity and local data balancing, revealing that the federated model achieves a strong predictive power with an ROC AUC of approximately 85%.