federated learning

Adaptive and Intelligent Security for Healthcare Databases in the United States: A Critical Narrative Review of Artificial Intelligence and Machine Learning Innovations, Threats and Policy Gaps

Background: Healthcare databases in the United States, including electronic health record systems, claims repositories, imaging archives and research data warehouses, have become high-value targets for ransomware, credential theft, insider misuse and privacy attacks. Artificial intelligence and machine learning are wid...

nio3000/one_shot_frontier: one_shot_frontier v1.0.0 — Manuscript Reproducibility Release

Research code and frozen reproducibility artifacts for decision identifiability in one-shot federated learning.

atomattias/TrustFed-RL: v1.0.1 — TrustFed-RL experiment source code

ource code accompanying the TrustFed-RL manuscript on multi-signal behavioural trust, trust-weighted federated learning, RL-based cyber response, and runtime governance for IoMT intrusion detection. This release includes: Core modules (src/): FL client/server, trust, governance, RL, adversary Locked experiment configs...

An Efficient Algorithm for Dynamic Client Selection in Federated Learning

mhnarfth/who-benefits-federation: INDIS 2026 camera-ready

Software archive accompanying the paper "Who Benefits from Federation? Heterogeneous Gains in Cross-Operator Network Traffic Representation Learning" by Mohammad Arafath Uddin Shariff and Byrav Ramamurthy (School of Computing, University of Nebraska-Lincoln), presented at the 13th International Workshop on Innovating t...

Integrated blockchain-federated learning framework for IOT security

The rapid expansion of the Internet of Things (IoT) introduces complex, multi-layered security challenges that centralized security models and conventional intrusion detection systems struggle to address. This paper presents a multi-layer security framework that integrates Blockchain for decentralized trust management,...

A Novel Federated and Quantum-resistant Security Framework for Edge-cloud Systems

As system decentralization heterogeneous resource constraints and the uncertain threat against quantum computing are introduced by rapid growth of edge-cloud computing infrastructures, this develop also raises new security challenges.Traditional security architectures depend on stationary cryptographic deployments and...

A Reinforcement Learning-Based Framework for Personalized Career Recommendations Among College Students

HRA-TS provides an effective and practical solution for personalized career recommendation in dynamic and privacy-sensitive environments and employs a hybrid long short-term memory–graph attention network encoder to jointly capture users’ evolving behavioral evolution and stable personal attributes.

Artificial intelligence and Machine learning in pharmacy and pharmaceutical technology

Artificial intelligence (AI) and Machine learning (ML) are transforming the pharmaceutical lifecycle—from target identification and lead optimization to clinical development, manufacturing, supply chain orchestration, and real-world pharmacovigilance. This review synthesizes recent advances (2018–2025) across core meth...

AI Models for Advancing Plant Tissue Culture and Bioreactors

Artificial intelligence (AI) is altering plant biotechnology by solving significant problems in tissue culture and bioreactor systems, including variability, man-power intensity, and poor reproducibility. Machine learning (ML) algorithms and artificial neural networks (ANNs) have opened the capability of simulating com...

Equivalence Between Nested Gibbs Measures and Log-Linear Combinations of Gibbs Measures

In this paper, three operations on Gibbs probability measures are studied. The first operation, often referred to as renormalization, takes one Gibbs probability measure and generates a new Gibbs measure by normalizing a power of its density. This normalization has a twofold effect: it changes the regularization factor...

Federated Learning with Spiking Neural Networks: A Lightweight and Privacy-Preserving Framework for Edge Intelligence

Federated Learning (FL) enables privacy-preserving distributed machine learning by keeping raw data local. However, conventional Artificial Neural Networks (ANNs) impose high computational and energy costs. Spiking Neural Networks (SNNs), inspired by biological neurons, provide energy-efficient computation via sparse s...