In the Industrial Internet of Things (IIoT), collaborative intelligence among distributed devices is essential for achieving autonomy and robustness, especially when facing non-IID and out-of-distribution (OOD) data. Deep neural networks have achieved significant success in various applications, but their prediction confidence often degrades on OOD data, which is critical in IIoT environments with heterogeneous sources. Centralized OOD detection methods assume data is centrally stored and require a large number of real OOD samples, which are impractical and costly in federated learning due to data silos and privacy issues. To address the above challenges, we formulate the new problem of OOD detection on heterogeneous data in a federated learning framework. We propose a novel multi-task optimal model named FOOD that improves OOD accuracy through optimal transport theory in a distributed manner with data privacy protection. Specifically, FOOD generates pseudo-OOD samples based on optimal transport theory and purifies training samples to enhance classification accuracy. We use the Wasserstein distance to measure the similarity between in-distribution and out-of-distribution samples and generate heterogeneous pseudo-OOD samples among different clients. FOOD is a plug-and-play plugin that can improve deep neural models' performance without introducing extra overhead. Experiments on OOD datasets show that FOOD significantly enhances OOD detection and classification on several public OOD datasets, AUROC improved by 1.69%, AUPR by 1.75%, and ACC by 4.67%. Using optimal transport theory, our work provides a practical approach to improving data generalization in generative models.

Federated domain generalization (FDG) enables collaborative learning across distributed devices to build a global prediction model capable of generalizing to diverse environments. While existing methods perform well on homogeneous data distributions, they struggle with performance degradation caused by data drift in heterogeneous settings. To address this challenge, we propose FedFAT, a novel method that leverages frequency domain adaptive interpolation to mitigate data drift effectively. FedFAT allows each client to adaptively exchange amplitude information for improved generalization while retaining phase information locally to ensure privacy. The interpolation process-including amplitude interpolation size, mask position, and fusion ratio-is dynamically determined based on the difference between the client's amplitude and that of the shared library. Additionally, we introduce amplitude normalization to align features across clients by batch-normalizing images from multi-source distributions, thereby reducing the negative impact of data drift. Building on these uniform features, weight perturbation is applied to ensure consistent low loss for local models. Extensive experiments and ablation studies on medical image analysis tasks, including MRI prostate segmentation and breast cancer tissue classification, demonstrate the superiority of FedFAT in handling data drift and improving generalization performance. Our results underscore the critical role of frequency domain adaptive interpolation in addressing data drift and enhancing the robustness of federated domain generalization.

Semi-supervised learning (SSL) has been prevailed in medical image analysis field because it leverage unlabeled data for training powerful models without incurring extra high annotation cost. However, the existing SSL models face the challenge of performance degradation in open-set scenario where both in-distribution (ID) and out-of-distribution (OOD) samples are mixed in unlabeled data. The existing two-phases methods treat OOD detection and semi-supervised classification as two independent tasks which fail to reveal their mutual reinforcement ability. In this research, we introduce a joint optimization framework designed to enhance both the semi-supervised classification task and the OOD detection task through an iterative process. Specifically, our approach employs a unified model to assess the likelihood of images being OOD sample, subsequently filtering these instances from the pool of unlabeled data. The model parameters update and the OOD detection are optimized alternately. Additionally, to avoid the model overconfidence, we introduce logit normalization (Logit-Norm) loss to provide more reliable predictions. To validate the effectiveness of our method, we use the ISIC2018 dataset as the ID dataset and mix OOD samples from other medical image datasets to train classification model. Experimental results demonstrate that our method successfully mitigates the influence of OOD data on semi-supervised medical image classification performance while also improving OOD detection performance. The proposed framework successfully addresses the challenges posed by OOD samples in semi-supervised learning, offering a promising solution for medical image classification tasks that involve OOD data.

High-quality magnetic resonance imaging plays a leading role in the diagnosis and treatment of brain tumors. Currently, a series of Transformer-based methods such as Hybrid Attention Transformer (HAT) shows good results in addressing the issue of brain tumor MRI super-resolution. However, researchers face significant challenges in generating high-resolution MRI of brain tumors for accurate disease diagnosis and treatment. Additionally, the high complexity of models and the substantial computational resources required further complicate clinical application. In order to address the above-mentioned problems, we propose a new Channel Attention Block (DCAB) to decrease the complexity, parameters, and computational cost of the model. In addition, we employ DCAB to brain tumors MRI super-resolution task and propose Dexterous Hybrid Attention Transformer (DHAT). DHAT combines self-attention, channel attention and overlapping cross-attention, which can be better suited for brain tumors MRI super-resolution tasks, further solving the problem of generating higher-resolution brain tumor MRI for clinical diagnosis and treatment. In comparison to the Channel Attention Block (CAB) in the HAT, DCAB demonstrates significant improvements in several key aspects: parameters, floating-point operations, latency, frames per second, and multiply-accumulate operations. Furthermore, experimental results indicate that DHAT is capable of generating higher-resolution brain tumors MRI compared to several existing reconstruction methods.

Federated Learning is a distributed learning paradigm that allows multiple organizations or devices to train a global model collaboratively in a privacy-preserving manner. However, there still exists privacy leakage risks due to the curious or dishonest server. In this paper, we propose a novel server-free federated learning paradigm named MChain-SFFL, which utilizes parallel multi-chain aggregation to mitigate privacy leakage risks and enhance convergence speed. First, MChain-SFFL randomly selects multiple users as chain heads. Then, MChain-SFFL utilizes parallel multi-chain communication mechanism to transmit the masked local model parameters. Finally, every chain head computes the model update for that chain and sends it to the other users. Upon receiving updates from all other chains, each user aggregates the received parameters to generate the model update for the current round. We validate the superiority of our method in accuracy and convergence speed on both image datasets and text datasets. Experimental results show that MChain-SFFL achieves superior privacy protection without impairing model accuracy and exhibits robustness to Non-IID data.