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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

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federated learning

1,863 papers

#federated learning Review Open access Aug 2026

Artificial Intelligence Techniques for Autonomous UAV Navigation: A Comprehensive Review of Perception, Path Planning, and Motion Control

This review provides a comprehensive synthesis of traditional and artificial intelligence (AI)-based techniques across the complete autonomous UAV navigation pipeline, including environmental perception, localization and mapping, path planning and obstacle avoidance, and motion control, together with commonly used data...

T. Mahmood, Ali Ahmed Mirza · 0 citations
#federated learning Open access Aug 2026

Federated Learning for Privacy-Preserving Data Analysis

This paper explores the application of federated learning (FL) as a novel approach to privacy-preserving data analysis. Traditional data analysis methods often require centralized data collection, raising significant privacy concerns. Federated learning offers a compelling alternative by enabling collaborative model tr...

Jincheng Zhang · 0 citations
#federated learning Open access Aug 2026

Distributed Generative Adversarial Networks with Federated Learning

This paper presents a novel approach to generative modeling by integrating Generative Adversarial Networks (GANs) with Federated Learning (FL). Traditional GAN training suffers from centralized data requirements and privacy concerns. This research addresses these limitations through a distributed GAN architecture speci...

Jincheng Zhang · 0 citations
#federated learning Open access Aug 2026

Information Bottleneck with Differential Privacy for Federated Learning

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing, thus addressing privacy concerns. However, FL is still susceptible to privacy breaches and suffers from significant information loss during model aggregation, a phenomenon a...

Jincheng Zhang · 0 citations
#federated learning Open access Aug 2026

Distributed Federated Learning with Byzantine Fault Tolerance via Blockchain Verification

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, FL systems are vulnerable to Byzantine attacks, where malicious participants can inject faulty model updates, compromising model accuracy and potentially causi...

Jincheng Zhang · 0 citations
#federated learning Open access Aug 2026

Distributed Federated Learning with Differential Privacy and Byzantine Fault Tolerance

Federated Learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, existing FL frameworks are susceptible to various vulnerabilities, including privacy breaches through information leakage and attacks from Byzantine clients a...

Jincheng Zhang · 0 citations
#federated learning Open access Aug 2026

AI‐Enabled 6G Space‐Air‐Ground–Integrated Networks for Ultra‐Reliable Low Latency Internet of Medical Things Healthcare

An AI‐assisted resource scheduling and cooperative learning model in a space–air–ground combined network (SAGIN) to possess ultra‐reliable low‐latency Internet of Medical Things applications and a multi agent—deep deterministic policy gradient (MA‐DDPG) agent that allocates resources in a distributed manner and a hiera...

T. Sardar, Gousia Thahniyath, Ahlam I. Almusharraf et al. · 0 citations
#federated learning Open access Aug 2026

Distributed Federated Learning with Byzantine Fault Tolerance via Blockchain Verification

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, FL systems are vulnerable to Byzantine attacks, where malicious participants can inject faulty model updates, compromising model accuracy and potentially causi...

Jincheng Zhang · 0 citations
#federated learning Open access Aug 2026

LACSF framework deployment for fault detection and mitigation

An experimental deployment of the proposed modular artificial intelligence framework showed improved detection accuracy and faster responsiveness compared to conventional diagnostic methods, highlighting the framework’s potential to improve fault diagnostics.

Faisal Shaikh, Sudipt Panta, R. R. Kumar et al. · 0 citations
#federated learning Open access Aug 2026

Differential Privacy for Federated Learning with Personalized Noise

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data, preserving data privacy. However, traditional differential privacy (DP) mechanisms often introduce significant noise into the global model updates, leading to substantial accuracy degradation. This paper prese...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

FedNeuroTwin: A Federated Edge-Neuromorphic Cognitive Digital Twin for Privacy-preserving EEG-based Attention State Learning NeuroSpike

Electroencephalography (EEG)-based Brain-Computer Interface (BCI) systems can support attention monitoring through non-invasive brain signal analysis.This study focuses on passive EEG attention classification for personalized cognitive tracking.A major problem is that EEG signals vary across clients, and non-independen...

Vijayakumar Kempuraj, C. Lakshmi · 0 citations
#federated learning Open access Aug 2026

DEx-FCL: Reproducibility package for adaptive Edge-IoT security management

Reproducibility package for the manuscript "DEx-FCL: Drift- and Explanation-Consistent Federated Continual Learning for Adaptive Edge-IoT Security Management." This release includes the DEx-FCL source code, deterministic federated partitioning utilities, fixed five-seed configurations, CICIoT2023 and Edge-IIoTset bench...

Arul Selvam P · 0 citations

From tech blogs

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Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

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.

MIT News · Artificial Intelligence Oct 7, 2026

Discovering the value of humanistic inquiry

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.

Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

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.

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