federated learning

Smart aquaponics: trends, challenges, and future directions

Future progress requires stronger integration of prediction, control, calibration, biological benchmarking, and operator usability, and this systematic literature review addresses this gap through 10 research questions.

Six-Hour Sepsis Prediction in Intensive Care: Patient-Level Nested Validation of Structured, Centralized Multimodal, and Simulated Federated Models

Federated Quantum-Inspired Tensor Compression for Energy-Efficient Edge Learning in Bandwidth-Constrained IoT Swarms

A privacy-preserving federated spatiotemporal dynamic graph neural network framework for epileptic seizure prediction

Abstract Epilepsy ranks among the most prevalent and debilitating neurological disorders, globally affecting an estimated 50 million individuals across all age groups and socioeconomic backgrounds [WHO, 2019]. In recent years, research on its prediction methods has made significant progress, driven by advancements in a...

Multimodal medical diagnosis: a mini review of LLM–vision fusion models in low-resource healthcare settings

Recent advances in large language models (LLMs) and vision transformers have enabled multimodal systems that integrate clinical text with medical imaging for diagnostic decision-making. While these systems show promising results on benchmark datasets in well-resourced research settings, their applicability in low-resou...

Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights

Emerging AI paradigms offer future-ready frameworks for personalized, privacy-preserving oncology care and multi-omics integration powered by AI enables the discovery of robust biomarkers for diagnosis, prognosis, and therapy response prediction.

Optimizing Byzantine Node Placement in Decentralized Federated Learning

This work introduces Byzantine Placement Influence (BPI), a set-level measure derived from the actual gossip dynamics that quantifies the cumulative exposure of honest nodes to Byzantine sources over the training horizon and shows that Byzantine placement is a critical but under-modeled dimension of DFL threat models a...

Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity

FedRoRA (Federated Rank-wise Personalized LoRA) is proposed, a novel framework that enables fine-grained personalization within rank-heterogeneous federations and consistently outperforms state-of-the-art methods.

Automated Vulnerability Patching in Legacy Code Using LLMs and Multi-AI Agents

While multi-agent LLM pipelines show real promise for legacy code specifically, since their capacity for autonomous context reconstruction and iterative, feedback-driven refinement directly addresses the sparse documentation and thin test coverage that define legacy environments, the literature to date has been validat...

Federated learning with multiple, intersectional and multiclass fairness guarantees under performance budgets

FedFairLAB is introduced, a FL method that enforces group, intersectional, and multiclass fairness simultaneously at both the local and global levels and a tunable performance budget allows practitioners to control how much predictive performance can be sacrificed to improve fairness.

Machine Learning Integration in Additive Manufacturing: From Process Optimization to Biomedical Implant Applications

The findings conclude that transitioning from ‘black-box’ models to explainable AI represents the definitive path forward for meeting FDA/CE regulatory standards in clinical validation.

Federated learning-based privacy-preserving multisource data fusion for smart grids

This study addresses the challenges of privacy leakage and data silos in multi-source heterogeneous data interaction within smart grids by designing a federated multi-source data fusion architecture that combines adaptive local differential privacy with feature space alignment. This architecture utilizes Hessian matrix...