Over the past few years, Federated Learning (FL) has emerged as a prominent paradigm for distributed model training, enabling collaborative learning across heterogeneous edge environments while inherently supporting privacy preservation. Although several frameworks have emerged to support FL workflows, deployment remains a complex and technically demanding phase, often requiring advanced expertise in system administration, virtualization, and networking. This complexity limits accessibility and slows down experimentation and adoption in broader research and industry contexts. To address these limitations, this work introduces a novel deployment toolkit designed to streamline the setup and execution of FL scenarios. Unlike existing solutions, our tool prioritizes ease of use, intuitive configuration, and a gentle learning curve, enabling users to design and launch sophisticated simulations and network topologies without requiring deep system-level knowledge. Through its modular architecture and lightweight orchestration model, the toolkit supports scalable deployments, complex federation strategies, and hybrid edge-cloud environments with minimal overhead.
The progressive digitization of Operation Technology (OT), thanks also to the integration of Industrial Internet of Things (IIoT) devices, is reshaping the landscape of modern industrial systems. This introduces additional complexity and amplifies the heterogeneity across hardware and software ecosystems, thus exacerbating the challenges of orchestration and lifecycle management, which become particularly intricate and demanding. To meet these challenges, modern DevOps practices, such as Continuous Integration and Continuous Deployment (CI/CD), runtime observability, and finegrained access control, are essential to shorten time-to-market, ensure service quality, manage operational complexity, while providing security guarantees. Moving a step toward seamless integration of OT into cloud-native ecosystems, we propose Kubernetes-based OT Orchestrator (KOTO), an orchestration platform that extends Kubernetes to enable comprehensive lifecycle management of OT devices in industrial environments. KOTO bridges the gap between IT and OT by seamlessly integrating with existing CI/CD pipelines and implementing Role-Based Access Control (RBAC) mechanisms tailored to device-level permissions. It abstracts the management of heterogeneous, multi-vendor hardware through a unified abstraction, enabling scalable and secure operations across diverse industrial setups. Without loss of generality, we demonstrate the use of the platform in managing Programmable Logic Controllers (PLCs). We then perform extensive evaluations in a realistic deployment environment, validating KOTO's effectiveness in enhancing operational resilience and responsiveness.
The paper presents a framework for bilingual terminology extraction targeting highly technical domains and the low-resource Kazakh language. We develop two unsupervised term annotators and introduce a hybrid Semantic NMF model that combines non-negative matrix factorization with multilingual embeddings (LaBSE, E5-base, BGE-M3). We further release Matcha, a BIO-annotated English and Kazakh corpus (similar to 3k terms) to fine-tune supervised extractors (BERT-BiLSTM-CRF, XLM-RoBERTa). Across ACTER and ACL RD-TEC 2.0 benchmarks and the new Matcha corpus, UA2 attains up to 51.8% F1, approaching supervised performance; supervised models reach about 70% F1 in Materials Science. The results highlight strong cross-lingual/cross-domain transfer for Semantic-NMF-based unsupervised methods and demonstrate a scalable path to terminology mining and downstream knowledge graph construction in under-represented languages.
Efficient workload forecasting is a key enabler of modern AIOps (Artificial Intelligence for IT Operations), supporting proactive and autonomous resource management across the computing continuum, from edge environments to large-scale cloud infrastructures. In this paper, we propose a Temporal Transformer architecture for CPU utilization prediction, designed to capture both short-term fluctuations and long-range temporal dependencies inworkload dynamics. The model is first pretrained on a large-scale Microsoft Azure VM dataset and subsequently fine-tuned on the Alibaba container dataset, enabling effective transfer learning across heterogeneous virtualization environments. Experimental results demonstrate that the proposed approach achieves high predictive accuracy while maintaining a compact model size and inference times compatible with real-time operation. Qualitative analyses further highlight the model's ability to reproduce workload patterns with high fidelity. These findings indicate that the proposed Temporal Transformer constitutes a lightweight and accurate forecasting component for next-generation AIOps pipelines, suitable for deployment across both cloud and edge intelligence scenarios.
Federated learning (FL) systems are vulnerable to client drift, where evolving client data distributions degrade global model performance. This work introduces DRAF, a drift-aware aggregation strategy that requires no access to client data and addresses both data drift and concept drift. DRAF employs two complementary metrics, a Population Stability Index (PSI)-based score for data drift and a prediction disagreement score for concept drift, combined into a dynamic regularization term to adjust client contributions during aggregation. Experiments on AWS infrastructure using the Flower framework and the CIFAR-10 dataset under controlled drift show that DRAF outperforms standard Federated Averaging (FedAvg), achieving up to a 0.5 reduction in global loss and an average 3% increase in accuracy, demonstrating improved robustness and convergence in non-stationary federated settings.
