Category
1,995 papers
Malware is one of the major threats to modern computer systems, mobile devices, cloud platforms, and networks. Traditional signature-based antivirus systems are effective against known malware but often have difficulty detecting new, polymorphic, obfuscated, and zero-day variants. Machine Learning (ML) provides a data-...
FedDCDS introduces a novel dual-phase secure federated learning framework designed to address the intertwined challenges of statistical heterogeneity, system heterogeneity, and security threats in large-scale edge computing environments. By integrating a dynamic spectral clustering mechanism, the framework dynamically...
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...
Reach audiences
Advertise in front of researchers, engineers, and readers.
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:...
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...
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by IBM Research on Hugging Face
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
