federated learning

Performance Analysis of a Decentralized Federated Learning System for Spoken-Command Recognition: Resilience and Security Considerations

The results indicate that the DFL system achieves accuracy comparable to centralized baselines in most scenarios, and that a cross-node validation mechanism reliably excludes poisoned updates as long as fewer than half of the nodes are compromised, clarify both the promise and the current limitations of decentralized,...

Artificial intelligence in precise surgical planning for adolescent idiopathic scoliosis: applications and challenges

Adolescent idiopathic scoliosis (AIS) is the most common three-dimensional spinal deformity among adolescents, characterized by a coronal curvature of > 10°, often accompanied by vertebral rotation and abnormal sagittal alignment, which can lead to severe cardiopulmonary and neurological dysfunction. For moderate-to-se...

Artificial Intelligence in Smart Photovoltaic Systems for High-Efficiency Energy Conversion: A Systematic Review

Artificial intelligence (AI) is transforming photovoltaic (PV) systems from passive generators into self-forecasting, self-diagnosing, and self-optimizing energy assets. This review synthesizes 170 Scopus-indexed documents published between 2020 and 2026 across six technical domains: solar resource and PV power forecas...

From data heterogeneity to convergence: A data-centric review of federated learning

Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning. This paradigm enables privacy with multiple clients to train a shared-task model collaboratively without exposing their local data. While being a key component in any learning system, data is also a primary source of vu...

Federated learning: From design foundations to advanced participant selection paradigms

Federated Learning (FL) has emerged as a promising paradigm owing to its significant advantages over conventional centralized machine learning approaches. It is a decentralized learning framework in which a global model is iteratively constructed by aggregating locally trained models from distributed clients, thereby e...

A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency

Federated learning (FL) has emerged as a secure paradigm for collaborative training among clients. Without data centralization, FL allows clients to share local information in a privacy-preserving manner. This approach has gained considerable attention, promoting numerous surveys to summarize the related works. However...

Federated learning for multi-institutional AI in healthcare via digital pathology

It is discussed how FL can enable scalable, privacy-preserving AI models, and future directions for standardizing and validating FL-based approaches in clinical workflows are outlined.

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...