federated learning

Beyond the AI Classroom: Epistemic Judgement Under Machine Advantage in Federated AI-Native Education

Most discussion of artificial intelligence in education treats AI as a tutor, writing assistant, assessment tool or means of personalisation while leaving the underlying educational architecture intact. This conceptual paper develops a different model. It distinguishes substitution AI, which performs an existing learne...

Integration of Artificial Intelligence and Blockchain: A Systematic Review of Applications, Architectures, Security, and Open Challenges

Abstract: The integration of artificial intelligence (AI) and blockchain combines adaptive computation with decentralized trust, cryptographic integrity, programmable transactions, and auditable data exchange. This systematic review synthesizes research on integration architectures, applications, reciprocal benefits, s...

FLEX: Flexible Federated Learning With Asynchronous Peer-to-Peer Communication and Adaptive Aggregation

Asynchronous peer to peer federated learning scheme with adaptive aggregation algorithm

AI-driven biosensing technology for early disease detection: A comprehensive review.

BackgroundArtificial intelligence (AI) is quickly changing the field of biosensing technologies to the point of early, real-time, and individualized disease detection and overcoming significant drawbacks of traditional biosensors, including noise interference, signal drift, and inability to process non-linear biologica...

FedMAST: Backdoor Detection and Containment in Federated Learning

FedMAST is the reproducibility artifact for the paper Backdoors Leave Structural Traces: FedMAST for Backdoor Detection and Containment in Federated Learning, accepted at the 38th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2026). The artifact contains the Flower-based federated learning...

Dataset and Experimental Results: Robust Aggregation Ranks the Free-Rider First

Prototype-based federated learning exchanges class-embedding matrices without exposing the computation that produced them. We evaluate three verification mechanisms against free-riding at this interface: robust aggregation used for contribution ranking, commitments to model behavior, and passport-derived identities. On...

FedMAST: Backdoor Detection and Containment in Federated Learning

FedMAST is the reproducibility artifact for the paper Backdoors Leave Structural Traces: FedMAST for Backdoor Detection and Containment in Federated Learning, accepted at the 38th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2026). The artifact contains the Flower-based federated learning...

VahanSWARM: A Blockchain-Driven Swarm Learning Framework for Secure Intelligent Vehicle Supply Chain Networks

Secure 6G IoT-Data Sharing Applications with Personalized Stackelberg Model and Deep Federated Learning on Blockchain

This approach aims to develop a lightweight authentication technique that ensures secure data sharing, prevents adversarial attacks, mitigates free-rider attacks, and optimizes privacy budgets while reducing computational overhead.

Federated versus Centralized Learning: A Comparative Analysis of CNN-Based Myocardial Infarction Classification

Adaptive entropy and robust aggregation for client selection in federated credit risk modeling

Federated learning enables collaborative modeling for financial risk prediction including credit default and transaction fraud without exposing raw local data. However, client heterogeneity, Non-IID data distribution, and dynamic participant involvement inevitably degrade model performance and incur excessive communi...

Dataset and Experimental Results: Robust Aggregation Ranks the Free-Rider First

Prototype-based federated learning exchanges class-embedding matrices without exposing the computation that produced them. We evaluate three verification mechanisms against free-riding at this interface: robust aggregation used for contribution ranking, commitments to model behavior, and passport-derived identities. On...