federated learning

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

Although substantial technical, regulatory, and ethical challenges remain unresolved, ongoing progress in hybrid modeling, federated learning, explainable AI, and evolving regulatory frameworks provides a plausible pathway toward individualized prediction and proactive management, provided that claims of clinical readi...

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...

Joao Ferreira project done at ISCTE IoT Laboratory

João Carlos Amaro Ferreira (ISCTE-IUL, 16/07/2021), showcasing the research group's work in IoT, edge/fog computing, and data science. The group has expertise in edge/fog architectures (open-source and commercial platforms), IoT communications (short and long range, lightweight protocols such as MQTT/CoAP, V2X), and di...

The Wisdom Continuum

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...

Algorithmic Bias Detection and Mitigation in Federated Learning

Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data, preserving data privacy. However, this approach is increasingly recognized to be vulnerable to algorithmic bias. The inherent diversity and potential imbalances within the data contributed by numerous clien...