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1,721 papers
CardioHealth-FL: Synthetic Cardiovascular Healthcare Dataset CardioHealth-FL is a rule-based synthetic healthcare dataset developed for cardiovascular risk and federated learning research. It contains 40,000 records, 16 features, and a target variable, Cardiovascular_Risk. The records were generated using predefined pr...
Initial public release of the code accompanying this PhD research: a federated learning (FedAvg/FedProx/FedDyn) framework for IoT intrusion detection, and the ReputationManager smart contract for active on-chain governance.
Decision Tree (DT) algorithms are among the most widely used supervised learning techniques in data mining and machine learning due to their high classification accuracy and interpretability. This survey presents a comprehensive review of decision tree methodologies, including classical algorithms such as ID3, C4.5, CA...
This registration presents the protocol for a prospective scoping review of the technical, implementation and governance evidence needed to design, evaluate and govern privacy-preserving multisite clinical diagnostic artificial intelligence platforms. The review will examine federated-learning architectures and aggrega...
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Wireless Sensor Networks have become essential infrastructure across environmental monitoring, healthcare, smart agriculture, industrial automation, and smart city applications. However, their distributed, resource-constrained, and physically accessible design creates serious security risks. This paper surveys cybersec...
Decision Tree (DT) algorithms are among the most widely used supervised learning techniques in data mining and machine learning due to their high classification accuracy and interpretability. This survey presents a comprehensive review of decision tree methodologies, including classical algorithms such as ID3, C4.5, CA...
This code-only package accompanies the manuscript “Federated Edge Learning over Optical-UAV Access Links With Deterministic-Payload Compression and Packetized Scheduling.”The software implements static mixed-resolution compression with error feedback (SMR-EF) and PacketFair scheduling for simulated federated learning o...
Federated Learning (FL) enables privacy-preserving distributed machine learning by keeping raw data local. However, conventional Artificial Neural Networks (ANNs) impose high computational and energy costs. Spiking Neural Networks (SNNs), inspired by biological neurons, provide energy-efficient computation via sparse s...
CardioHealth-FL: Synthetic Cardiovascular Healthcare Dataset CardioHealth-FL is a rule-based synthetic healthcare dataset developed for cardiovascular risk and federated learning research. It contains 40,000 records, 16 features, and a target variable, Cardiovascular_Risk. The records were generated using predefined pr...
Modern transportation systems are shaped by multiple interdependent modes of transport. Understanding their interactions is essential for effective management and planning. Cross-modal flow and demand predictions can improve forecasting accuracy for individual modes by exploiting these interdependencies. However, data-...
AbstractFederated learning (FL) allows organizations to collaborate on Android malware detection without sharing raw samples. However, malicious client updates can affect the global model, while model updates can also expose information about private training data. We present FedSecureXAI, a multimodal federated framew...
AbstractFederated learning (FL) allows organizations to collaborate on Android malware detection without sharing raw samples. However, malicious client updates can affect the global model, while model updates can also expose information about private training data. We present FedSecureXAI, a multimodal federated framew...
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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