federated learning

Optimizing resource allocation for federated LLM training via workload forecasting and autoscaling in edge environments

An integrated framework that combines LLM fine-tuning using federated adaptive local low-rank adaptation (FedALoRA) with forecasting-driven autoscaling policies is proposed, making it well-suited for federated-LLM Autoscaling in dynamic edge environments.

FedLLMsHydraNet: federated LLMs-driven model to enhance privacy-preserving and decision-making in financial credit risk prediction

A Novel Approach for Conducting Asynchronous Federated Learning with Blockchain Integration

Reputation-Driven Federated Learning: Enhancing Trust in Healthcare Data Sharing with Blockchain

Machine Learning and Blockchain in Peer-to-Peer Energy Trading: A Cross-Layer Review of Functional Roles, Market Operation, Trust, and Privacy

Peer-to-peer (P2P) energy trading combines local energy resources, market coordination, data-driven decisions, and transaction management. This review examines how machine learning (ML) and blockchain are used across these functions and separates market and ledger processes from physical electricity delivery. A structu...

AI in Predictive Healthcare II: A Ten-Domain Structured Evidence Review of Wearable Artificial Intelligence, 2024–2026

This expanded edition re-evaluates and extends a prior structured evidence review of artificial intelligence (AI) in wearable predictive healthcare. Where the original review verified evidence across four domains — atrial fibrillation, continuous glucose monitoring, federated learning and human digital twins — this edi...

Friend-Safe Adversarial Attack for selective evasion in personalized federated learning

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy; however, the personalization of client models in non-independent and identically distributed (non-IID) settings creates an unexplored attack surface where adversarial examples can selectively fool spec...

[Global Livingry Governance Architecture | Part 12]: Autonomous Tsunami Early Warning and Emergency Response System Architecture: Integration of Community Camera Networks, Subsea Station UAVs, Security Systems, Edge AI, and Tensor Stress Index (TSI_{t+1})

Disaster management and mitigation in coastal regions and tectonic fault zones require a fundamental shift from manual, expert-committee-dependent response models to closed-loop automated architectures. By combining the Tensor Stress Index function (TSI_{t+1}), Edge AI processing, distributed community observation netw...

Deep Learning and Ensemble Learning Techniques for Breast Cancer Detection: A Review of Recent Advances

Breast cancer remains the most frequently diagnosed cancer and a leading cause of cancer death among women worldwide, so tools that support early and accurate detection are urgently needed. This review synthesizes a pool of one hundred studies, largely published between 2023 and 2026, on deep learning, ensemble learnin...

Policy-Gated Zero-Trust Federated Learning: Identity-Bound Enrollment, Replay-Resistant Control, and Secure Coordination

Native Byzantine-Robust Aggregation for Trustworthy Federated Learning: A C++20 Evaluation of Krum, Multi-Krum, Trimmed Mean, and Coordinate-wise Median

Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development

To bridge the gap between in silico predictions, chemical synthesis, and biological reality, emerging solutions such as federated learning, causality-aware Explainable AI, closed-loop autonomous laboratories, and efficiency-oriented algorithmic strategies are explored.