The rapid development of artificial intelligence (AI), cloud computing, data-intensive applications, and low-code development platforms is changing the architectural requirements of modern information systems. Low-code technologies reduce the amount of manually written software code and enable a wider group of users to...
Problem-to-Outcome Architecture is a federated methodology for preserving continuity from an operational problem signal to measurable deployment outcomes and reusable knowledge. It separates problem discovery from technology-task formulation; links persistent problems to continuous technology intelligence; supports bid...
Azamat Karbuzov· Zenodo (CERN European Organi...· 0 citations
Federated learning trains shared models without centralizing local data, but repeated model exchange can become a bottleneck in bandwidth-, energy-, and latency-constrained edge systems. This paper presents FEDCODE, a communication-aware federated learning simulation framework for reproducible evaluation of update repr...
E. Guberović, Igor Čavrak· Italian National Conference...· 0 citations
The rapid growth of Internet of Things (IoT) deployments has widened the network attack surface while increasing the cost of centralised intrusion detection, particularly when raw traffic must be moved to remote servers. We propose HFL-SDN-IDS, a hierarchical, resource-aware framework that combines a lightweight federa...
The global financial system processes trillions of dollars in transactions daily, creating an urgent need for monitoring systems that are secure, scalable, privacy-preserving, and capable of detecting sophisticated fraud in real time. Despite a decade of development, existing approaches are fundamentally fragmented: ru...
Ozgur Salam Gurbuz, M. Salam· Proceedings of the 7th Natio...· 0 citations
The emergence of Internet of Things (IoT) devices in next-generation communication networks has brought about new and complex challenges related to resource management, which include massive connectivity, heterogeneous traffic loads, and strict energy considerations. Static and heuristic resource allocation algorithms...
Nitish Kumar, Mohammad Shahbaz Khan· International Journal of Sci...· 0 citations
Federated Learning (FL) enables collaborative training of models across institutions without centralizing sensitive data, making it well-suited for privacy-concerned applications, such as medical imaging. To protect FL model updates during secure aggregation, additive masking is commonly employed. However, its underlyi...
Ivan Donà, H. H. Brunner, Á. T. Olivas et al.· 0 citations
Federated multi-player multi-armed bandit problems model collaborative sequential decision-making where multiple players interact with a common bandit environment and share information through a central server to accelerate learning. Existing federated bandit frameworks typically assume that all players willingly share...
This paper revisits the distributed learning problem for training a multinomial logistic regression model with the Federated Averaging ($\texttt{FedAvg}$) algorithm. We concentrate on a scenario with arbitrarily large stepsizes and heterogeneous update rules where the devices may perform a different number of local upd...
Hematological disorders, including anemia and leukemia, pose major health risks globally, especially in developing areas. Around 1.92 billion people are affected by anemia, with about 475,000 new leukemia cases diagnosed yearly, resulting in approximately 310,000 deaths. The reliance on subjective conventional diagnost...
Bhavadharani Babu, S. Umapathy· The Egyptian Journal of Inte...· 0 citations
Experimental results demonstrate that the proposed FMADRL with digital-twin assistance significantly outperforms existing methods in energy savings, reduced delay, improved reliability, and robustness against security threats.
B. A, T. Sadasivam· Discover Computing· 0 citations
Federated learning (FL) enables collaborative training of medical image segmentation models without sharing raw patient data, yet existing approaches assume a homogeneous compute budget across institutions, limiting participation of low-resource sites. We propose Fed-ADApt, a depth-adaptive federated framework for UNet...
Abhijeet Parida, Zhi-Fan Jiang, Pooneh Roshanitabrizi et al.· 0 citations
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
