federated 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

On Agent-SafetyBench, R-Judge, and AgentDojo, federated FGLGuard exceeds the in-domain centralized ceiling on all three benchmarks without pooling any data---where unsupervised anomaly guards and local-only training fail.

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.

FedHC-GNN for Federated Multimodal Representation Learning of Cross-Modal Disclosure Consistency in Online Charity Transparency Assessment

Privacy-Preserving Intrusion Detection via Federated Learning: An Empirical Evaluation with Automated Response Recommendation

Distributed Federated Learning with Byzantine Fault Tolerance using Homomorphic Encryption

Federated learning (FL) offers a promising approach to training machine learning models across decentralized devices while preserving data privacy. However, FL systems are vulnerable to Byzantine faults, where malicious participants introduce corrupted model updates, compromising the integrity of the global model. This...

Distributed Learning with Federated Generative Adversarial Networks

This paper presents a novel framework for distributed learning of Generative Adversarial Networks (GANs) through a federated learning approach. Traditional GAN training demands centralized computation and large-scale datasets, posing significant challenges for privacy-sensitive and resource-constrained environments. Th...

Algorithmic Fairness via Randomized Smoothing in Federated Learning

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent data heterogeneity and potential biases present in these datasets pose significant challenges to achieving algorithmic fairness. This paper invest...

"Processed Results for Low-Visible Robust Federated Learning"

"This dataset contains the processed experimental results and figure-source files associated with the study \u201cLow-Visible Robust Weight Generation for Byzantine Federated Learning via Sketch-Based Detection.\u201d The data support the evaluation of CipherSketch-RFL under Byzantine model-update attacks, including si...