federated learning

Federated Continual Learning for Encrypted Traffic Classification at the Network Edge Under Asynchronous Concept Drift

Concept drift can degrade encrypted-traffic classifiers deployed at the network edge as applications, protocols, and usage patterns evolve. This paper formulates federated continual learning under asynchronous real- and virtual drift and proposes DriftGuard, a framework combining a two-level per-node detector, selectiv...

Privacy-Aware Guarded Federated Scorebank Ensembles: Software and Reproducibility Package

This record provides the version 1.0.0 software and reproducibility package supporting the manuscript Privacy-Aware Guarded Federated Scorebank Ensembles for Unsupervised Edge Insider-Threat Detection. The implementation studies heterogeneous cross-silo collaboration using three CMU CERT release-based clients. It inclu...

When Does Trust Help? A Leakage-Free Evaluation of Representation- and Aggregation-Based Defenses Against Backdoors in Federated Intrusion Detection

Federated learning (FL) lets distributed intrusion-detection systems (IDS) train on private traffic, but exposes the global model to poisoning and backdoor attacks under non-IID client data. A popular defense family scores or filters clients using a small trusted "root" dataset and the similarity of client behaviour to...

Trustworthy and privacy-preserving decentralized federated learning with multi-layer defense for secure collaborative AI

These findings demonstrate that the proposed framework provides an effective balance between privacy preservation, adversarial robustness, and trustworthy decentralized collaborative learning for secure AI-driven systems.

aliakarma/FedFaith-GNN: v1.0.1

Blockchain-Anchored Provenance for Federated Learning under Variable IoT Device Availability

Optimizing Federated Learning Using Graph Neural Networks

Uncertainty-Aware Bayesian Federated Learning for Low-Resource Neural Machine Translation at the Edge

Edge translation systems must balance limited computation, data locality, and weak performance on low-resource language pairs. This study presents Multitask Bayesian Federated Learning (MT-BayesFL), a multilingual translation framework that combines a lightweight shared encoder, task-specific decoders, matrix-normal po...

Privacy-Aware Guarded Federated Scorebank Ensembles: Software and Reproducibility Package

This record provides the version 1.0.0 software and reproducibility package supporting the manuscript Privacy-Aware Guarded Federated Scorebank Ensembles for Unsupervised Edge Insider-Threat Detection. The implementation studies heterogeneous cross-silo collaboration using three CMU CERT release-based clients. It inclu...

Wisdom contiuum

The Wisdom Continuum: A Unified Theory of Human Development, Learning, Intelligence, Artificial Intelligence, and Civilizational Evolution Description / Abstract The Wisdom Continuum presents a civilizational-scale framework that unifies human development, moral philosophy, educational reform, and artificial intelligen...

Privacy-preserving federated IoT intelligence for multimodal airway liberation decision support via offline reinforcement learning

Cross-fusion of digital twins and artificial intelligence in diabetes: from mechanistic elucidation to full-cycle precision management

The integration of digital twins (DT) and artificial intelligence (AI) is driving a paradigm shift in diabetes mellitus (DM) care from traditional population-based, symptomatic intervention to a lifecycle-oriented precision management approach. This review systematically elaborates on the cross-disciplinary integration...

FedQS: asynchronous federated learning based on queue scheduling

An asynchronous framework named FedQS, which employs a multi-dimensional staleness evaluation mechanism that dynamically assesses updates by combining the similarity between local and global models with client latency metrics, and implements a decoupling solution via a queue scheduling algorithm to resolve the coupling...