FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity

Federated Learning (FL) provides decentralised model training, which effectively tackles problems such as distributed data and privacy preservation. However, the generalisation of global models frequently faces challenges from data heterogeneity among clients. This challenge becomes even more pronounced when datasets are limited in size and class imbalance. To address data heterogeneity, we propose a novel method, \textit{FedQuad}, that explicitly optimises smaller intra-class variance and larger inter-class variance across clients, thereby decreasing the negative impact of model aggregation on the global model over client representations. Our approach minimises the distance between similar pairs while maximising the distance between negative pairs, effectively disentangling client data in the shared feature space. We evaluate our method on the CIFAR-10 and CIFAR-100 datasets under various data distributions and with many clients, demonstrating superior performance compared to existing approaches. Furthermore, we provide a detailed analysis of metric learning-based strategies within both supervised and federated learning paradigms, highlighting their efficacy in addressing representational learning challenges in federated settings.

Robust Federated Learning in the Face of Covariate Shift: A Magnitude Pruning with Hybrid Regularization Framework for Enhanced Model Aggregation

The development of highly sophisticated neural networks has allowed for fast progress in every field of computer vision, however, applications where annotated data is prohibited due to privacy or security concerns remain challenging. Federated Learning (FL) offers a promising framework for individuals aiming to collaboratively develop a shared model while preserving data privacy. Nevertheless, our findings reveal that variations in data distribution among clients can profoundly affect FL methodologies, primarily due to instabilities in the aggregation process. We also propose a novel FL framework to mitigate the adverse effects of covariate shifts among federated clients by combining individual parameter pruning and regularization techniques to improve the robustness of individual clients' models to aggregate. Each client's model is optimized through magnitude-based pruning and the addition of dropout and noise injection layers to build more resilient decision pathways in the networks and improve the robustness of the model's parameter aggregation step. The proposed framework is capable of extracting robust representations even in the presence of very large covariate shifts among client data distributions and in the federation of a small number of clients. Empirical findings substantiate the effectiveness of our proposed methodology across common benchmark datasets, including CIFAR10, MNIST, SVHN, and Fashion MNIST. Furthermore, we introduce the CelebA-Gender dataset, specifically designed to evaluate performance on a more realistic domain. The proposed method is capable of extracting robust representations even in the presence of both high and low covariate shifts among client data distributions.

The Bad Batches: Enhancing Self-Supervised Learning in Image Classification Through Representative Batch Curation

The pursuit of learning robust representations without human supervision is a longstanding challenge. The recent advancements in self-supervised contrastive learning approaches have demonstrated high performance across various representation learning challenges. However, current methods depend on the random transformation of training examples, resulting in some cases of unrepresentative positive pairs that can have a large impact on learning. This limitation not only impedes the convergence of the learning process but the robustness of the learnt representation as well as requiring larger batch sizes to improve robustness to such bad batches. This paper attempts to alleviate the influence of false positive and false negative pairs by employing pairwise similarity calculations through the Fr\'echet ResNet Distance (FRD), thereby obtaining robust representations from unlabelled data. The effectiveness of the proposed method is substantiated by empirical results, where a linear classifier trained on self-supervised contrastive representations achieved an impressive 87.74\% top-1 accuracy on STL10 and 99.31\% on the Flower102 dataset. These results emphasize the potential of the proposed approach in pushing the boundaries of the state-of-the-art in self-supervised contrastive learning, particularly for image classification tasks.