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1,737 papers
Reliability metadata improve federated aggregation when they predict the value of a client update for other participants when they predict the value of a client update for other participants using auditable cross-client update-utility certificates.
This study proposes a federated learning framework, the Hierarchical Attention Fusion Network (HAFN) combined with the FedNova aggregation strategy for privacy-preserving multimodal stress classification, and demonstrates robustness and effectiveness of the proposed framework.
Rotavirus is a viral infection that affect mostly children below 5 years of age. It remains a leading cause of acute gastroenteritis and diarrheal morbidity among children under five years globally, despite vaccine availability. It causes inflammation of the digestive traits which results in diarrhea, vomiting and feve...
The CUST-Iris dataset was collected at Capital University of Science and Technology (CUST), Islamabad, Pakistan, for biometric iris recognition research. The dataset contains 2,880 iris images acquired from 720 unique irises (L+R) using the Crossmatch I-Scan 2 iris scanner. Images were captured in the near-infrared (NI...
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With the continuous expansion of the supply chain scale and the widespread adoption of distributed architectures, how to achieve cross-node inventory collaborative optimization while protecting the data privacy of all participants has become a key challenge. To address this challenge, this paper proposes a distributed...
Neonatal sepsis remains a major cause of morbidity and mortality worldwide, while timely diagnosis continues to be challenging because of nonspecific clinical manifestations and limitations of conventional diagnostic methods. Recent advances in artificial intelligence (AI) have created new opportunities for the early p...
The complexity of emerging cyber threats renders the need for intelligent, adaptive and real-time responses essential. In this paper, an explainable deep learning assisted federated learning and adversarial robustness-based AI-enabled threat detection scheme for cybersecurity is introduced. The framework also breaks th...
The CUST-Iris dataset was collected at Capital University of Science and Technology (CUST), Islamabad, Pakistan, for biometric iris recognition research. The dataset contains 2,880 iris images acquired from 720 unique irises (L+R) using the Crossmatch I-Scan 2 iris scanner. Images were captured in the near-infrared (NI...
This paper studies federated learning for nonparametric regression in the context of distributed samples across different servers, each adhering to distinct differential privacy constraints. The setting we consider is heterogeneous, encompassing both varying sample sizes and differential privacy constraints across serv...
The global imperative to decarbonise road transport has required the automotive and energy sectors to reconceptualise vehicular powertrain engineering across all scales, from atomic-level electrochemistry to fleet-level grid interaction. This PRISMA-compliant systematic review synthesises 120 peer-reviewed publications...
The results show that colluding semi-honest nodes can recover the original local updates of honest nodes, enabling downstream reconstruction of private training data, and demonstrate that SA alone does not guarantee privacy in DFL when local aggregation induces asymmetric observations.
To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) exploits waveform superposition over multiple-access channels (MACs) for analog model aggregation. However, coherent AirFL typically relies on stringent PHY-layer condition...
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
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.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
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