AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,836 papers
Decentralized Federated Learning (DFL) offers a promising approach to machine learning in resource-constrained environments like sensor networks, where data resides locally and communication is limited. However, traditional federated learning paradigms often fall short in providing robust privacy guarantees and are sus...
Predictive maintenance aims to anticipate equipment failures, minimizing downtime and maintenance costs. Traditional approaches often rely on centralized data collection, raising significant privacy concerns, particularly when dealing with sensitive sensor data from industrial equipment. This research proposes a novel...
Federated learning (FL) offers a privacy-preserving approach to collaborative machine learning by enabling model training across decentralised health datasets without sharing sensitive individual-level data. This study investigated the fairness and reliability of FL for predicting antenatal care (ANC) utilisation in lo...
FedShield-IDS is proposed, a novel federated intrusion detection framework that integrates a hybrid one-dimensional Convolutional Neural Network with Long Short-Term Memory units to simultaneously capture spatial traffic fingerprints and long-range temporal attack dynamics across IoT edge devices.
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Federated learning (FL) offers a privacy-preserving approach to collaborative machine learning by enabling model training across decentralised health datasets without sharing sensitive individual-level data. This study investigated the fairness and reliability of FL for predicting antenatal care (ANC) utilisation in lo...
Decentralized Federated Reinforcement Learning (DFRL) presents a promising approach to training robust and adaptable RL agents by leveraging distributed data and computational resources. However, this paradigm is susceptible to attacks from Byzantine actors who can inject malicious updates, jeopardizing the learning pr...
This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...
This paper presents a novel framework for distributed learning of Generative Adversarial Networks (GANs) through a federated learning approach. Traditional GAN training demands centralized computation and large-scale datasets, posing significant challenges for privacy-sensitive and resource-constrained environments. Th...
This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...
Decentralized Federated Learning (DFL) offers a promising approach to machine learning in resource-constrained environments like sensor networks, where data resides locally and communication is limited. However, traditional federated learning paradigms often fall short in providing robust privacy guarantees and are sus...
The Satellite Internet of Things (SIoT) leverages low Earth orbit (LEO) satellite constellations to support wide-area, real-time, and intelligent services such as disaster monitoring, ship tracking, and environmental sensing. These tasks demand collaborative model training across multiple satellites to improve predicti...
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, achieving strong differential privacy (DP) guarantees in FL is a significant challenge, particularly when combined with compression techniques commonly employ...
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
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.
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