Learn From Zoom: Decoupled Supervised Contrastive Learning For WCE Image Classification

Accurate lesion classification in Wireless Capsule Endoscopy (WCE) images is vital for early diagnosis and treatment of gastrointestinal (GI) cancers. However, this task is confronted with challenges like tiny lesions and background interference. Additionally, WCE images exhibit higher intra-class variance and inter-class similarities, adding complexity. To tackle these challenges, we propose Decoupled Supervised Contrastive Learning for WCE image classification, learning robust representations from zoomed-in WCE images generated by Saliency Augmentor. Specifically, We use uniformly down-sampled WCE images as anchors and WCE images from the same class, especially their zoomed-in images, as positives. This approach empowers the Feature Extractor to capture rich representations from various views of the same image, facilitated by Decoupled Supervised Contrastive Learning. Training a linear Classifier on these representations within 10 epochs yields an impressive 92.01% overall accuracy, surpassing the prior state-of-the-art (SOTA) by 0.72% on a blend of two publicly accessible WCE datasets. Code is available at: https://github.com/Qiukunpeng/DSCL.

Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning

Federated Learning (FL) is an emerging distributed machine learning approach that preserves client privacy by storing data on edge devices. However, data heterogeneity among clients presents challenges in training models that perform well on all local distributions. Recent studies have proposed clustering as a solution to tackle client heterogeneity in FL by grouping clients with distribution shifts into different clusters. However, the diverse learning frameworks used in current clustered FL methods make it challenging to integrate various clustered FL methods, gather their benefits, and make further improvements. To this end, this paper presents a comprehensive investigation into current clustered FL methods and proposes a four-tier framework, namely HCFL, to encompass and extend existing approaches. Based on the HCFL, we identify the remaining challenges associated with current clustering methods in each tier and propose an enhanced clustering method called HCFL+ to address these challenges. Through extensive numerical evaluations, we showcase the effectiveness of our clustering framework and the improved components. Our code will be publicly available.

FedConceptEM: Robust Federated Learning Under Diverse Distribution Shifts

Federated Learning (FL) is a machine learning paradigm that protects privacy by keeping client data on edge devices. However, optimizing FL in practice can be challenging due to the diversity and heterogeneity of the learning system. Recent research efforts have aimed to improve the optimization of FL with distribution shifts, but it is still an open problem how to train FL models when multiple types of distribution shifts, i.e., feature distribution skew, label distribution skew, and concept shift occur simultaneously. To address this challenge, we propose a novel algorithm framework, FedConceptEM, for handling diverse distribution shifts in FL. FedConceptEM automatically assigns clients with concept shifts to different models, avoiding the performance drop caused by these shifts. At the same time, clients without concept shifts, even with feature or label skew, are assigned to the same model, improving the robustness of the trained models. Extensive experiments demonstrate that FedConceptEM outperforms other state-of-the-art cluster-based FL methods by a significant margin.

FedAug: Reducing the Local Learning Bias Improves Federated Learning on Heterogeneous Data

Federated Learning (FL) is a machine learning paradigm that learns from data kept locally to safeguard the privacy of clients, whereas local SGD is typically employed on the clients' devices to improve communication efficiency. However, such a scheme is currently constrained by the slow and unstable convergence induced by clients' heterogeneous data. In this work, we identify three under-explored phenomena of the biased local learning that may explain these challenges caused by local updates in supervised FL. As a remedy, we propose FedAug, a novel unified algorithm that reduces the local learning bias on features and classifiers to tackle these challenges. FedAug consists of two components: AugMean and AugCA. AugMean alleviates the bias in the local classifiers by balancing the output distribution of models. AugCA learns client invariant features that are close to global features but considerably distinct from those learned from other input distributions. In a series of experiments, we show that FedAug consistently outperforms other SOTA FL and domain generalization (DG) baselines, in which both two components (i.e., AugMean and AugCA) have individual performance gains.

InvNorm: Domain Generalization for Object Detection in Gastrointestinal Endoscopy

Domain Generalization is a challenging topic in computer vision, especially in Gastrointestinal Endoscopy image analysis. Due to several device limitations and ethical reasons, current open-source datasets are typically collected on a limited number of patients using the same brand of sensors. Different brands of devices and individual differences will significantly affect the model's generalizability. Therefore, to address the generalization problem in GI(Gastrointestinal) endoscopy, we propose a multi-domain GI dataset and a light, plug-in block called InvNorm(Invertible Normalization), which could achieve a better generalization performance in any structure. Previous DG(Domain Generalization) methods fail to achieve invertible transformation, which would lead to some misleading augmentation. Moreover, these models would be more likely to lead to medical ethics issues. Our method utilizes normalizing flow to achieve invertible and explainable style normalization to address the problem. The effectiveness of InvNorm is demonstrated on a wide range of tasks, including GI recognition, GI object detection, and natural image recognition.

Towards Federated Learning on Time-Evolving Heterogeneous Data

Federated Learning (FL) is an emerging learning paradigm that preserves privacy by ensuring client data locality on edge devices. The optimization of FL is challenging in practice due to the diversity and heterogeneity of the learning system. Despite recent research efforts on improving the optimization of heterogeneous data, the impact of time-evolving heterogeneous data in real-world scenarios, such as changing client data or intermittent clients joining or leaving during training, has not been well studied. In this work, we propose Continual Federated Learning (CFL), a flexible framework, to capture the time-evolving heterogeneity of FL. CFL covers complex and realistic scenarios -- which are challenging to evaluate in previous FL formulations -- by extracting the information of past local datasets and approximating the local objective functions. Theoretically, we demonstrate that CFL methods achieve a faster convergence rate than \fedavg in time-evolving scenarios, with the benefit being dependent on approximation quality. In a series of experiments, we show that the numerical findings match the convergence analysis, and CFL methods significantly outperform the other SOTA FL baselines.