federated learning

A review of lightweight intrusion detection and security mechanisms for resource constrained IoT and WSNs

Resource-constrained Internet of Things (IoT) devices and wireless sensor networks (WSNs) remain difficult to secure using conventional security mechanisms because their sensing, forwarding, and processing nodes operate with limited memory, computation, energy, bandwidth, and software-update capability. These limits ex...

Safe and Privacy-Aware AI Models for Medical Image Processing: A Multi-Paradigm Comparative Study

In addition to producing better models, hospitals that combine patient scans into a single training server also provide a single, alluring target: in 2023 alone, over 130 million healthcare records were compromised globally. This research begins with this contradiction between the confidentiality requirements of medici...

Safe and Privacy-Aware AI Models for Medical Image Processing: A Multi-Paradigm Comparative Study

In addition to producing better models, hospitals that combine patient scans into a single training server also provide a single, alluring target: in 2023 alone, over 130 million healthcare records were compromised globally. This research begins with this contradiction between the confidentiality requirements of medici...

A Reproducible Privacy-Preserving Federated Learning Framework for CRM Decision Systems

This repository contains the reproducibility materials for the FedCRM-DP study, a privacy-preserving cross-silo federated learning framework combining federated optimization, per-client record-level differential privacy, and SecAgg+ secure aggregation. The package contains source code, frozen experimental configuration...

Privacy-Preserving Machine Learning at the Edge: A Comparative Study of Federated Learning, Differential Privacy, and Secure Aggregation

The increasing use of artificial intelligence on smartphones, Internet of Things devices, edge servers, and other distributed platforms has created new opportunities for intelligent applications but has also intensified concerns regarding the privacy of machine-learning data. Conventional centralized machine learning r...

Beyond Diagnostic Accuracy: Calibration and Predictive Uncertainty of Artificial Intelligence Models for Oral and Dental Disease Diagnosis

A Systematic Review Protocol “Beyond Diagnostic Accuracy: Calibration and Predictive Uncertainty of Artificial Intelligence Models for Oral and Dental Disease Diagnosis” Title of the ReviewBeyond Diagnostic Accuracy: Calibration and Predictive Uncertainty of Artificial Intelligence Models for Oral and Dental Disease Di...

Privacy-Preserving Machine Learning at the Edge: A Comparative Study of Federated Learning, Differential Privacy, and Secure Aggregation

The increasing use of artificial intelligence on smartphones, Internet of Things devices, edge servers, and other distributed platforms has created new opportunities for intelligent applications but has also intensified concerns regarding the privacy of machine-learning data. Conventional centralized machine learning r...

Federated AI-Enabled 6G IoT Framework for Real-Time Accident Detection and Emergency Response in Intelligent Transportation Systems (ITS)

Adaptive Toggling of Architectural Patterns for Federated Learning

Failure Propagation and Self-Correction in Multi-Agent LLM Systems: How Deliberative Consensus, Credit Assignment, and Architectural Isolation Jointly Determine Systemic Reliability

This version corrects the use of a withdrawn preprint. Version 2 cited the CONCAT framework (arXiv:2605.29612) in Section 3.4 and reported its results of up to 2.02x higher efficiency and a 50.1% latency reduction. On 2026-09-22 its authors withdrew the manuscript, writing: "We identified a potential issue in the repea...

A Secure and Efficient Federated Learning Privacy Protection Scheme Based on DCT and Top-k Sparsity

Capability-Aware Distributed Learning and Inference for Heterogeneous 6G Edge Environments

The rapid evolution of sixth-generation (6G) wireless networks is increasing the demand for AI-enabled edge intel ligence that can operate across devices with different capabil ities, resource constraints, and availability. Existing distributed learning paradigms, including Federated Learning (FL) and Split Learning (S...