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1,772 papers
This paper investigates the application of decentralized machine learning (DML) to autonomous vehicle control. Traditional centralized approaches to autonomous driving often face challenges related to scalability, communication bottlenecks, and vulnerability to single points of failure. This research proposes a novel s...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, current FL frameworks are susceptible to attacks from malicious participants, known as Byzantine attacks, which can compromise model integrity and p...
This paper investigates the application of federated learning and differential privacy for decentralized anomaly detection in network traffic. Traditional anomaly detection methods often rely on centralized data collection, posing significant privacy risks and hindering scalability. This research proposes a novel frame...
Federated learning (FL) offers a promising approach to distributed machine learning by enabling collaborative model training without directly exchanging raw data. However, traditional FL systems are plagued by privacy vulnerabilities and significant communication costs. This paper proposes a novel framework, Quantum Wa...
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This paper proposes a novel approach to network anomaly detection leveraging the strengths of federated learning and graph neural networks (GNNs). Traditional centralized anomaly detection systems suffer from single points of failure and privacy concerns. Our decentralized system addresses these limitations by enabling...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data while preserving data privacy. However, existing privacy-preserving techniques frequently rely on approximations and heuristics, often lacking rigorous mathematical justification. This work presents a novel inf...
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data without direct data sharing. However, the traditional approach of using static homomorphic encryption (HE) schemes for FL introduces significant overheads, particularly when combined with the iterative nature...
This paper presents a novel approach to decentralized knowledge graph construction using Bayesian Networks and Federated Learning. The core idea is to distribute the knowledge graph construction process across a network of nodes, each maintaining a local Bayesian Network. Federated Learning is then employed to iterativ...
Custom code for the manuscript "Direction-shaped anisotropic pre-DP gradient perturbation and clinically grounded gradient-inversion evaluation for differentially private federated medical imaging." Implements a three-tier federated learning pipeline (FedProx + Opacus DP-SGD + a pre-DP perturbation shield) and evaluate...
Custom code for the manuscript "Direction-shaped anisotropic pre-DP gradient perturbation and clinically grounded gradient-inversion evaluation for differentially private federated medical imaging." Implements a three-tier federated learning pipeline (FedProx + Opacus DP-SGD + a pre-DP perturbation shield) and evaluate...
Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, the inherent trust relationships within FL systems are complex and often overlooked. This paper presents a formal model for analyzing trust dynamics in FL, incorpor...
This paper investigates the application of federated learning and differential privacy for decentralized anomaly detection in network traffic. Traditional anomaly detection methods often rely on centralized data collection, posing significant privacy risks and hindering scalability. This research proposes a novel frame...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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.
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