federated learning

Distributed Learning with Federated Bayesian Networks

This paper presents a novel approach to distributed learning leveraging Federated Bayesian Networks (FBNs). Traditional federated learning methods struggle with capturing complex, non-independent data distributions, often leading to suboptimal global models. We introduce a framework where multiple devices collaborative...

Title: Self-Aware Distributed Computing for Algorithmic Discovery

The automation of algorithmic discovery, a critical component of innovation across numerous fields, traditionally relies on the expertise of human researchers and engineers. This paper introduces a novel approach to algorithmic discovery leveraging self-aware distributed computing, specifically employing a federated le...

An Intelligent Framework for Secure and Scalable Child Abuse Data Management Using Machine Learning and Blockchain

Child abuse remains a critical global concern requiring efficient, secure, and scalable data management systems to support early detection and intervention. However, existing centralized approaches suffer from data fragmentation, limited interoperability, and significant privacy and security challenges. This paper prop...

Starfish-FL, an Agentic Federated Learning and Analysis Framework

Starfish-FL is a general-purpose federated learning and analysis platform that orchestrates distributed computation across healthcare institutions without exposing patient-level data. Standardized statistical scripts execute locally behind each institution's firewall and only site-level aggregate outputs are shared for...

MedSparseFL: Sparse and Privacy-Preserving Federated Learning for Healthcare Applications

This project provides processed data indexes for reproducing experiments from the MedSparseFL study, a sparse and privacy-preserving federated learning framework for healthcare applications. The datasets include CheXpert and HAM10000. For each dataset, CSV files (chexpert_index.csv and ham10000_index.csv) contain:Image...

Traffic road control and lane allocation framework for intelligent transportation system using federated deep reinforcement learning

Resilient Multi-Layer LSTM Digital Twin Framework for Athlete Logistics and Safety in 6G

The development of 6G communication technologies provides new possibilities for the integration of advanced intelligence, connectivity, and security in sports and athlete management. In this paper, a Federated AI and Privacy-Preserving Digital Twin system (FPP-DT) is presented to optimize athlete logistics, performance...

A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications

Background. Federated learning (FL) has gained wide popularity as a collaborative learning paradigm enabling collaborative AI in sensitive healthcare applications. Nevertheless, the practical implementation of FL presents technical and organizational challenges, as it generally requires complex communication infrastruc...

Pushing the (Decision) Boundaries: Dynamically Calibrating Differentially Private Noise to Explainability in Federated Learning

XCal-FL is proposed, a closed-loop, explainability-driven local training algorithm for image classification in cross-silo FL that dynamically calibrates DP noise from three complementary signals, suggesting explainability is a distinct dimension of the privacy trade-off that cannot be inferred from utility alone, with...

Privacy, Robustness, and Fairness Trade-offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface

This paper introduces geometric indistinguishability as a conceptual lens for a regime in which privacy-induced dispersion in client updates can make minority-class signals harder for robust aggregation to preserve and suggests that aggregation-aware modeling and sample-aware evaluation are promising directions for tru...

Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learning

Topology-guided safeguards for LLM-based multi-agent systems (MAS) train a GNN over the inter-agent communication graph to localize risky agents and intervene on the topology---but they assume one operator can pool all labeled traces. Across organizations that assumption breaks: episodes contain private prompts, tool o...

Govern the Model, Not Only the Data: Storage, Circulation, and Learning in Creative AI

It is argued that federation is not in itself a remedy for extractive AI, because outcomes depend on who governs the data and the model and who has agency over the practices that shape them, and four design principles for a creative data commons are proposed.