AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,836 papers
As digital evidence continues to grow at an alarming rate, the use of machine learning (ML) in digital forensics presents an unparalleled opportunity to improve the efficiency of investigations while lowering environmental and ethical costs. This chapter examines the developing concept of sustainable green forensics, w...
The detection of fake news and artificial intelligence (AI)-generated synthetic content has become one of the central challenges of contemporary information science. While deep learning approaches—particularly transformer-based language models and convolutional networks—have achieved strong performance on established b...
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by grounding responses in external documents retrieved at inference time. In federated RAG (FedRAG), documents are distributed across multiple autonomous corpora that cannot be centrally aggregated due to privacy, regulatory, or organizational c...
This free sample edition contains two full chapters of "Reliable AI at Scale: Data Engineering for the Intelligent Systems Era," a book about the data systems artificial intelligence actually stands on. The book's argument: AI systems are data-intensive systems, and their failures are data failures. Included chapters:...
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Federated learning (FL) trains models across geographically dispersed clients whose electricity carries very different carbon intensities. We present FedGreenCS, a carbon-aware approach to FL for IoT and next-generation networks. Clients are first grouped by data distribution similarity, using pairwise cosine similarit...
The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling continuous monitoring, remote diagnosis, and data-driven decision-making. However, conventional cloud-centric IoMT architectures face significant limitations, including high latency, bandwidth overhead, privacy leakage, and increased exposu...
Cardiovascular disease (CVD) is one of the leading causes of death in the world. To get proper treatment we need to detect it early on. This paper researches the latest developments in using Machine Learning (ML) and Internet of Things (IoT) technologies to detect Cardiovascular Disease. It compares different models fr...
Graph federated learning (FL) allows multiple agents to collaboratively train a global graph model while preserving data privacy. However, current methods overlook the issue of low-quality or malicious data contributions, which can degrade the global model’s performance. This paper addresses this gap by proposing a nov...
This paper analyzes the vulnerabilities of privacy-preserving algorithms applied to multidimensional medical and nutritional databases. Syntactic anonymization models (k-anonymity, l-diversity, t-closeness), differential privacy and federated learning are compared. It is shown that each of them is exposed to specific a...
This paper presents a joint bandwidth allocation and federated learning optimization framework for Software-Defined Networking (SDN)-assisted autonomous vehicular networks. The framework addresses challenges such as dynamic channel conditions, mobility-induced fading, and heterogeneous data by minimizing convergence bo...
This paper presents OmniGuard V2X, a security prototype for connected and au-tonomous vehicles. The system integrates blockchain auditing, zero-knowledge privacy, decentral-ized identity, post-quantum key establishment, federated learning, and an encrypted forensic black box into a unified architecture. Rather than mak...
The fifth issue of the ELASTIC project newsletter marks the project's final stretch towards its conclusion in February 2027, as the consortium moves from mid-term results to system-level integration and validation. This issue covers ELASTIC's participation at EuCNC & 6G Summit 2026 in Málaga (Booth 76), where both demo...
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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