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Federated Learning (FL) enables decentralized model training while preserving data privacy but remains susceptible to poisoning attacks. Malicious clients can manipulate local data or model updates, threatening FL's reliability, especially in privacy-sensitive domains like healthcare and finance. While client-side optimization algorithms play a crucial role in training local models, their resilience to such attacks is underexplored. This study empirically evaluates the robustness of three widely used optimization algorithms: SGD, Adam, and RMSProp-against label-flipping attacks (LFAs) in image classification tasks using Convolutional Neural Networks (CNNs). Through 900 individual runs in both federated and centralized learning (CL) settings, we analyze their performance under Independent and Identically Distributed (IID) and Non-IID data distributions. Results reveal that SGD is the most resilient, achieving the highest accuracy in 87% of cases, while Adam performs best in 13%. Additionally, centralized models outperform FL on CIFAR-10, whereas FL excels on Fashion-MNIST, highlighting the impact of dataset characteristics on adversarial robustness.

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This report describes the ICDAR 2025 Competition on Automatic Classification of Literary Epochs (ICDAR 2025 CoLiE), which consisted of two tasks focused on automatic prediction of the time in which a book was written (date of first publication). Both tasks comprised two sub-tasks, where a related fine-grained classification was addressed. Task 1 consisted of the identification of literary epochs, such as Romanticism or Modernism (sub-task 1.1), and a more precise classification of the period within the epoch (sub-task 1.2). Task 2 addressed the chronological identification of century (sub-task 2.1) or decade (sub-task 2.2). The compiled dataset and the reported findings are valuable to the scientific community and contribute to advancing research in the automatic dating of texts and its applications in digital humanities and temporal text analysis.

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