Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract.

Problem: Artificial intelligence has been widely investigated for diagnosis and treatment strategy design, with some models proposed for detecting oral pharyngeal, nasopharyngeal, or laryngeal carcinoma. However, no comprehensive model has been established for these regions.

Aim: Our hypothesis was that a common pattern in the cancerous appearance of these regions could be recognized and integrated into a single model, thus improving the efficacy of deep learning models.

Privacy preserving federated learning for full heterogeneity.

Federated learning is a novel distribute machine learning paradigm to support cooperative model training among multiple participant clients, where each client keeps its private data locally to protect its data privacy. However, in practical application domains, Federated learning still meets several heterogeneous challenges such data heterogeneity, model heterogeneity, and computation heterogeneity, significantly decreasing its global model performance. To the best of our knowledge, existing solutions only focus on one or two challenges in their heterogeneous settings.

Backdoor attacks against distributed swarm learning.

Traditional machine learning approaches often need a central server, where raw datasets or model updates are trained or aggregated in a centralized way. However, these approaches are vulnerable to many attacks, especially by the malicious server. Recently, a new distributed machine learning paradigm, called Swarm Learning (SL), has been proposed to support no-central-server based decentralized training.