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. METHODS: We utilized a point-wise spatial attention network model to perform semantic segmentation in these regions. RESULTS: Our study demonstrated an excellent outcome, with an average mIoU of 86.3%, and an average pixel accuracy of 96.3%. CONCLUSION: The research confirmed that the mucosa of oral pharyngeal, nasopharyngeal, and laryngeal regions may share a common appearance, including the appearance of tumors, which can be recognized by a single artificial intelligence model. Therefore, a deep learning model could be constructed to effectively recognize these tumors.

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. In each training round, each participant node has a chance to be selected to serve as a temporary server. Thus, these participant nodes do not need to share their private datasets to ensure a fair and secure model aggregation in a central server. To the best of our knowledge, there are no existing solutions about the security threats in swarm learning. In this paper, we investigate how to implant backdoor attacks against swarm learning to illustrate its potential security risk. Experiment results confirm the effectiveness of our method with high attack accuracies in different scenarios. We also study several defense methods to alleviate these backdoor attacks.

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. In this paper, to address the above challenges simultaneously, we present a novel solution called Full Heterogeneous Federated Learning (FHFL). Firstly, we propose a synthetic data generation approach to mitigate the Non-IID data heterogeneity problem. Secondly, we use knowledge distillation to learn from heterogeneous models of participant clients for model aggregation in the central server. Finally, we produce an opportunistic computation schedule strategy to exploit the idle computation resources for fast-computing clients. Experiment results on different datasets show that our FHFL method can achieve an excellent model training performance. We believe it will serve as a pioneer work for distributed model training among heterogeneous clients in Federated learning.