Trusted-Data-Guided Label Enhancement on Noisy Labels

FedELR: When federated learning meets learning with noisy labels

Existing research on federated learning (FL) usually assumes that training labels are of high quality for each client, which is impractical in many real-world scenarios (e.g., noisy labels by crowd-sourced annotations), leading to dramatic performance degradation. In this work, we investigate noisy FL through the lens of early-time training phenomenon (ETP). Specifically, a key finding of this paper is that the early training phase varies among different local clients due to the different noisy classes in each client. In addition, we show that such an inconsistency also exists between the local and global models. As a result, local clients would always begin to memorize noisy labels before the global model reaches the optimal, which inevitably leads to the degradation of the quality of service in real-world FL applications (e.g. tumor image classification among different hospitals). Our findings provide new insights into the learning dynamics and shed light on the essence cause of this degradation in noisy FL. To address this problem, we reveal a new principle for noisy FL: it is necessary to align the early training phases across local models. To this end, we propose FedELR, a simple yet effective framework that aims to force local models to stick to their early training phase via an early learning regularization (ELR), so that the learning dynamics of local models can be kept at the same pace. Moreover, this also leverages the ETP in local clients, leading each client to take more training steps in learning a more robust local model for optimal global aggregation. Extensive experiments on various real-world datasets also validate the effectiveness of our proposed methods.

Label Distribution Learning by Partitioning Label Distribution Manifold

Researchers have suggested leveraging label correlation to deal with the exponentially sized output space of label distribution learning (LDL). Among them, some have proposed to exploit local label correlation. They first partition the training set into different groups and then exploit local label correlation on each one. However, these works usually apply clustering algorithms, such as -means, to split the training set and obtain the clustering results independent of label correlation. The structures (e.g., low rank and manifold) learned on such clusters may not efficiently capture label correlation. To solve this problem, we put forward a novel LDL method called LDL by partitioning label distribution manifold (LDL-PLDM). First, it jointly bipartitions the training set and learns the label distribution manifold to model label correlation. Second, it recurses until the reconstruction error of learning the label distribution manifold cannot be reduced. LDL-PLDM achieves label-correlation-related partition results, on which the learned label distribution manifold can better capture label correlation. We conduct extensive experiments to justify that LDL-PLDM statistically outperforms state-of-the-art LDL methods.

Partial Multilabel Learning Using Noise-Tolerant Broad Learning System With Label Enhancement and Dimensionality Reduction

Partial multilabel learning (PML) addresses the issue of noisy supervision, which contains an overcomplete set of candidate labels for each instance with only a valid subset of training data. Using label enhancement techniques, researchers have computed the probability of a label being ground truth. However, enhancing labels in the noisy label space makes it impossible for the existing partial multilabel label enhancement methods to achieve satisfactory results. Besides, few methods simultaneously involve the ambiguity problem, the feature space's redundancy, and the model's efficiency in PML. To address these issues, this article presents a novel joint partial multilabel framework using broad learning systems (namely BLS-PML) with three innovative mechanisms: 1) a trustworthy label space is reconstructed through a novel label enhancement method to avoid the bias caused by noisy labels; 2) a low-dimensional feature space is obtained by a confidence-based dimensionality reduction method to reduce the effect of redundancy in the feature space; and 3) a noise-tolerant BLS is proposed by adding a dimensionality reduction layer and a trustworthy label layer to deal with PML problem. We evaluated it on six real-world and seven synthetic datasets, using eight state-of-the-art partial multilabel algorithms as baselines and six evaluation metrics. Out of 144 experimental scenarios, our method significantly outperforms the baselines by about 80%, demonstrating its robustness and effectiveness in handling partial multilabel tasks.

Federated Data Quality Assessment Approach: Robust Learning With Mixed Label Noise

Federated learning (FL) has been an effective way to train a machine learning model distributedly, holding local data without exchanging them. However, due to the inaccessibility of local data, FL with label noise would be more challenging. Most existing methods assume only open-set or closed-set noise and correspondingly propose filtering or correction solutions, ignoring that label noise can be mixed in real-world scenarios. In this article, we propose a novel FL method to discriminate the type of noise and make the FL mixed noise-robust, named FedMIN. FedMIN employs a composite framework that captures local-global differences in multiparticipant distributions to model generalized noise patterns. By determining adaptive thresholds for identifying mixed label noise in each client and assigning appropriate weights during model aggregation, FedMIN enhances the performance of the global model. Furthermore, FedMIN incorporates a loss alignment mechanism using local and global Gaussian mixture models (GMMs) to mitigate the risk of revealing samplewise loss. Extensive experiments are conducted on several public datasets, which include the simulated FL testbeds, i.e., CIFAR-10, CIFAR-100, and SVHN, and the real-world ones, i.e., Camelyon17 and multiorgan nuclei challenge (MoNuSAC). Compared to FL benchmarks, FedMIN improves model accuracy by up to 9.9% due to its superior noise estimation capabilities.