最近，联邦学习（FL）获得了深入的研究，因为它具有为分散客户提供协作训练机器学习模型的数据隐私的能力。通常，部署了参数服务器（PS）来汇总不同客户端贡献的模型参数。分散的联合学习（DFL）已从FL升级，该学习允许客户直接与邻居聚集模型参数。 DFL对于车辆网络特别可行，因为车辆以车辆到车辆（V2V）方式相互通信。但是，由于车辆路线和通信距离的限制，单个车辆很难与他人充分交流模型。促成单个车辆模型的数据源可能没有足够多样化，从而导致模型准确性差。为了解决这个问题，我们提出了DFL-DDS（带有多元化数据源）算法的DFL-DDS，以使DFL中的数据源多样化。具体而言，每辆车都保持状态向量以记录每个数据源对其模型的贡献权重。采用Kullback-Leibler（KL）差异来衡量国家向量的多样性。为了提高DFL的收敛性，车辆通过最大程度地减少其状态向量的KL差异来调整每个数据源的聚合权重，并且可以在理论上证明其在多元化数据源中的有效性。最后，通过广泛的实验（使用MNIST和CIFAR-10数据集）评估DFL-DDS的优势，这些实验表明DFL-DD可以加速DFL的收敛性，并显着提高模型的准确性，并显着提高与最先进的盆地相比。

最近，基于区块链的联合学习（BFL）引起了密集的研究关注，因为培训过程是可审核的，并且该体系结构无助于避免了Vanilla Federated学习（VFL）中参数服务器的单点故障。然而，BFL大大升级了通信流量量，因为BFL客户端获得的所有本地模型更新（即，模型参数的更改）都将转移给所有矿工进行验证以及所有客户端以进行聚合。相比之下，参数服务器和VFL中的客户端仅保留汇总模型更新。因此，BFL的巨大沟通流量将不可避免地损害培训效率，并阻碍BFL现实的部署。为了提高BFL的实用性，我们是第一个通过压缩BFL中的通信（称为BCFL）来提出基于快速区块链的联合学习框架的人之一。同时，我们得出了BCFL的收敛速率，而非凸损失损失。为了最大化最终模型的准确性，我们进一步提出问题，以最大程度地减少收敛率的训练损失，而相对于压缩率和块生成速率的训练时间有限，这是BI-CONVEX优化问题，可以是有效解决。最后，为了证明BCFL的效率，我们对标准CIFAR-10和女权主义数据集进行了广泛的实验。我们的实验结果不仅验证了我们的分析的正确性，而且还表明BCFL可以显着将通信流量降低95-98％，或者与BFL相比，训练时间缩短了90-95％。

卷积神经网络（CNN）和变压器在多媒体应用中取得了巨大成功。但是，几乎没有努力有效，有效地协调这两个架构以满足图像的范围。本文旨在统一这两种架构，以利用其学习优点来降低图像。特别是，CNN的局部连通性和翻译等效性以及变压器中自我注意力（SA）的全球聚合能力被完全利用用于特定的局部环境和全球结构表示。基于雨水分布揭示降解位置和程度的观察，我们在帮助背景恢复之前引入退化，并因此呈现关联细化方案。提出了一种新型的多输入注意模块（MAM），以将降雨的去除和背景恢复关联。此外，我们为模型配备了有效的深度可分离卷积，以学习特定的特征表示并权衡计算复杂性。广泛的实验表明，我们提出的方法（称为ELF）的表现平均比最先进的方法（MPRNET）优于0.25 dB，但仅占其计算成本和参数的11.7 \％和42.1 \％。源代码可从https://github.com/kuijiang94/magic-elf获得。

联合学习（FL）引发了高通信开销，这可以通过压缩模型更新而大大缓解。然而，网络环境中压缩和模型精度之间的权衡仍不清楚，为简单起见，大多数实现仅采用固定压缩率。在本文中，我们首次系统地检查了该权衡，识别压缩误差对最终模型精度的影响，相对于学习率。具体而言，我们将每个全局迭代的压缩误差因其强大凸面和非凸损耗下的收敛速度分析。然后，我们通过策略性地调整每次迭代中的压缩速率来提高最终模型精度来最大化最终模型精度的适应框架。我们讨论了具有代表压缩算法的实用网络中框架的关键实施问题。对流行的MNIST和CIFAR-10数据集的实验证实，我们的解决方案有效地降低了网络流量，但在FL中保持了高模型精度。

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. To rigorously evaluate the robustness and generalizability of current approaches, we propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic segmentation models, especially spanning different input representations (e.g., point clouds, voxels, projected images, and etc.), network architectures and training schemes. Through this study, we obtain two insights: 1) We find out that the input representation plays a crucial role in robustness. Specifically, under specific corruptions, different representations perform variously. 2) Although state-of-the-art methods on LiDAR semantic segmentation achieve promising results on clean data, they are less robust when dealing with noisy data. Finally, based on the above observations, we design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications. It is promising that our benchmark, comprehensive analysis, and observations can boost future research in robust LiDAR semantic segmentation for safety-critical applications.

Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. However, their iterative refinement process in high-dimensional data space results in slow inference speed, which restricts their application in real-time systems. Previous works have explored speeding up by minimizing the number of inference steps but at the cost of sample quality. In this work, to improve the inference speed for DDPM-based TTS model while achieving high sample quality, we propose ResGrad, a lightweight diffusion model which learns to refine the output spectrogram of an existing TTS model (e.g., FastSpeech 2) by predicting the residual between the model output and the corresponding ground-truth speech. ResGrad has several advantages: 1) Compare with other acceleration methods for DDPM which need to synthesize speech from scratch, ResGrad reduces the complexity of task by changing the generation target from ground-truth mel-spectrogram to the residual, resulting into a more lightweight model and thus a smaller real-time factor. 2) ResGrad is employed in the inference process of the existing TTS model in a plug-and-play way, without re-training this model. We verify ResGrad on the single-speaker dataset LJSpeech and two more challenging datasets with multiple speakers (LibriTTS) and high sampling rate (VCTK). Experimental results show that in comparison with other speed-up methods of DDPMs: 1) ResGrad achieves better sample quality with the same inference speed measured by real-time factor; 2) with similar speech quality, ResGrad synthesizes speech faster than baseline methods by more than 10 times. Audio samples are available at https://resgrad1.github.io/.

Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e.g., Uber, Airbnb). Over the years, crowdsourcing has morphed from providing a platform where workers and tasks can be matched up manually into one which leverages data-driven algorithmic management approaches powered by artificial intelligence (AI) to achieve increasingly sophisticated optimization objectives. In this paper, we provide a survey presenting a unique systematic overview on how AI can empower crowdsourcing - which we refer to as AI-Empowered Crowdsourcing(AIEC). We propose a taxonomy which divides algorithmic crowdsourcing into three major areas: 1) task delegation, 2) motivating workers, and 3) quality control, focusing on the major objectives which need to be accomplished. We discuss the limitations and insights, and curate the challenges of doing research in each of these areas to highlight promising future research directions.

Fine-grained classification and counting of bone marrow erythroid cells are vital for evaluating the health status and formulating therapeutic schedules for leukemia or hematopathy. Due to the subtle visual differences between different types of erythroid cells, it is challenging to apply existing image-based deep learning models for fine-grained erythroid cell classification. Moreover, there is no large open-source datasets on erythroid cells to support the model training. In this paper, we introduce BMEC (Bone Morrow Erythroid Cells), the first large fine-grained image dataset of erythroid cells, to facilitate more deep learning research on erythroid cells. BMEC contains 5,666 images of individual erythroid cells, each of which is extracted from the bone marrow erythroid cell smears and professionally annotated to one of the four types of erythroid cells. To distinguish the erythroid cells, one key indicator is the cell shape which is closely related to the cell growth and maturation. Therefore, we design a novel shape-aware image classification network for fine-grained erythroid cell classification. The shape feature is extracted from the shape mask image and aggregated to the raw image feature with a shape attention module. With the shape-attended image feature, our network achieved superior classification performance (81.12\% top-1 accuracy) on the BMEC dataset comparing to the baseline methods. Ablation studies also demonstrate the effectiveness of incorporating the shape information for the fine-grained cell classification. To further verify the generalizability of our method, we tested our network on two additional public white blood cells (WBC) datasets and the results show our shape-aware method can generally outperform recent state-of-the-art works on classifying the WBC. The code and BMEC dataset can be found on https://github.com/wangye8899/BMEC.

With the development of natural language processing techniques(NLP), automatic diagnosis of eye diseases using ophthalmology electronic medical records (OEMR) has become possible. It aims to evaluate the condition of both eyes of a patient respectively, and we formulate it as a particular multi-label classification task in this paper. Although there are a few related studies in other diseases, automatic diagnosis of eye diseases exhibits unique characteristics. First, descriptions of both eyes are mixed up in OEMR documents, with both free text and templated asymptomatic descriptions, resulting in sparsity and clutter of information. Second, OEMR documents contain multiple parts of descriptions and have long document lengths. Third, it is critical to provide explainability to the disease diagnosis model. To overcome those challenges, we present an effective automatic eye disease diagnosis framework, NEEDED. In this framework, a preprocessing module is integrated to improve the density and quality of information. Then, we design a hierarchical transformer structure for learning the contextualized representations of each sentence in the OEMR document. For the diagnosis part, we propose an attention-based predictor that enables traceable diagnosis by obtaining disease-specific information. Experiments on the real dataset and comparison with several baseline models show the advantage and explainability of our framework.