federated learning

AI-Powered Anomaly Detection in Cloud-Based Applications

The findings suggest that AI-powered anomaly detection significantly strengthens observability and security in cloud-based applications, enabling proactive threat mitigation and operational optimization in increasingly complex distributed environments.

Information Governance as a Dynamic Capability for Data-Driven AI Innovation: A Systematic Literature Review

This study addresses the gap between abstract ethical principles and practical, distributed demands of modern AI governance through a PRISMA 2020 guided systematic review of 78 peer-reviewed studies published between 2020 and 2024, aiming to synthesise a cohesive architecture for modern AI governance.

Physics-informed federated scientific machine learning for constitutive modeling under heterogeneous and decentralized data conditions

Robust security enhancements approach for privacy-preserving federated learning under poisoning attacks

EdurLDP-FL: Differential privacy for accelerated convergence in federated learning

FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation

FedCMAPSS is introduced, a benchmark for federated RUL estimation based on the commonly-used NASA C-MAPSS dataset, to provide a standard foundation for developing and comparing federated predictive maintenance solutions.

The Rise of Artificial Intelligence in Oncology: Opportunities, Challenges, and Future Directions

Overall, AI is becoming an integral component of modern oncology, particularly radiation oncology, and its successful integration into routine clinical practice will require robust validation, transparent governance, equitable implementation, and continued clinician oversight to ensure safe, effective, and patient-cent...

A Federated Learning Framework for Privacy-Preserving Oral Cancer Screening on Smartphones

This work presents a practical FL framework that supports geographically distributed collaboration among AI healthcare researchers and facilitates the development of robust models for oral cancer screening.

LumiCharge: Spherical Harmonic Convolutional Networks for Atomic Charge Prediction in Drug Discovery.

This work proposes LumiCharge, a novel atomic charge prediction framework that incorporates high-order spherical harmonics convolutions and explicitly models multibody interactions, and demonstrates exceptional extrapolation capability and robustness across molecules of varying sizes, effectively overcoming the limitat...

PepBAN: A Deep Learning Framework with Bilinear Attention and Adversarial Learning for Peptide-Protein Interaction Prediction

PepBAN is introduced, a deep learning framework for modeling PepPI predictions that effectively learns the pattern of pairwise local interactions, enables the identification of key residues participating in the peptide-protein interactions, and offers an intuitive approach to interpret the underlying mechanisms of PepP...

SynGFN: learning across chemical space with generative flow-based molecular discovery

This study proposes SynGFN, which models molecular design as a cascade of simulated chemical reactions, enabling the assembly of molecules from synthesizable building blocks, as a bridge linking molecular design and synthesis, accelerating exploration and producing diverse, synthesizable, high-performance molecules.

ChargeNet: E(3) Equivariant Graph Attention Network for Atomic Charge Prediction

This work introduces an advanced equivariant graph attention neural network specifically engineered to model long-range atomic electrostatic interactions with high precision, and improves the model's accuracy, generalization, and robustness in complex scenarios.