We introduce FLInterrupt, an open-source, browser-based federated learning (FL) simulator for research on involuntary client interruption during synchronous FedAvg. The backend runs communication rounds in PyTorch. A React dashboard lets researchers set datasets and models, interrupt or reconnect clients at runtime, re...
Tudor-Mihai David, Mihai Udrescu· SoftwareX· 0 citations
Trust management under adversarial uncertainty is a central challenge in distributed learning systems. We propose Verifiable FL with Two-Stage Selection (VFL-TS), a knowledge-driven framework for Byzantine-resilient federated learning that maintains a dynamic trust knowledge base—updated through cryptographically verif...
Omar Dib· Zenodo (CERN European Organi...· 0 citations
Federated Learning (FL) enables collaborative intrusion detection across distributed Internet of Things (IoT) edge devices without centralizing private network telemetry. However, existing frameworks assume static data distributions and fail under non-stationary conditions: emerging zero-day attack families cause sever...
AlHayan Abdullah· Zenodo (CERN European Organi...· 0 citations
Space-to-ground collaborative remote sensing has become a promising mode for global-scale earth observation. However, the open satellite-ground communication link faces severe security threats, especially when unauthorized eavesdroppers deploy gradient or feature inversion attacks based on deep learning to reconstruct...
Kun Wang, Yuan Gao, Yuanqiao Zhang et al.· Remote Sensing· 0 citations
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The AI-based learning analytics is revolutionizing STEAM learning by facilitating adaptable, information-based, and individualised learning space. The chapter discusses the application of machine learning, natural language processing and predictive analytics to multimodal data on learners to create dynamic learner prof...
Shilpa Aarthi, R. N. Ravikumar· Advances in computational in...· 0 citations
The El-Rakhawi Architecture for Predictive Biosensing and Precision Therapeutics (EAPBPT) by Dr. Mohamed Kamal Arafa El-Rakhawi (DOI: 10.5281/zenodo.23084200) integrates six validated technologies for pre-symptomatic disease detection and personalized treatment: multi-omic liquid biopsy, lab-on-a-chip microfluidics, we...
m el-rakhawi· Zenodo (CERN European Organi...· 0 citations
The rapid digitization of healthcare has led to an explosion of patient data, necessitating
advanced analytics for improved diagnostics, treatment, and predictive modeling. However,
traditional centralized data processing poses significant privacy and security risks,
particularly concerning sensitive patient informatio...
Olabode Michael Soneye· WORLD JOURNAL OF INNOVATION...· 0 citations
Trust management under adversarial uncertainty is a central challenge in distributed learning systems. We propose Verifiable FL with Two-Stage Selection (VFL-TS), a knowledge-driven framework for Byzantine-resilient federated learning that maintains a dynamic trust knowledge base—updated through cryptographically verif...
Omar Dib· Zenodo (CERN European Organi...· 0 citations
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Federated learning enables breast-imaging sites to jointly train mammography artificial intelligence (AI) without sharing images, but radiologists at each site must still annotate selected images during active-learning rounds. We developed and evaluated a client-adaptive vision-language gatekeeper that withhold...
Adea Nesturi, D. Gaviria, Jia-Jun Zeng et al.· Journal of the American Coll...· 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