federated learning

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

The review concludes that optimized, explainable, and adversarially robust ensemble frameworks, validated through multi-center clinical trials, represent the most promising direction for translating deep learning research into dependable and deployable breast cancer detection systems.

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.

Chest disease detection using federated multi-scale graph capsule network with light spectrum and dual attention

The new MACBDN approach is highly successful in integrating federated learning, graph representations, and the attention mechanism to maintain an optimal balance between the three parameters, and its lightweight nature and privacy-preserved design help it stay flexible for practical clinical settings and multi-party ho...

A machine learning framework for enhancing threat detection in core banking systems

One of the biggest issues around using machine learning (ML) for cybersecurity in commercial banking is the gap between proof-of-concept and in production. Not every model that has a high accuracy score on a benchmark dataset is the same model as one that is operating in a live transaction pipeline, subject to the mu...

A Cross-Layer Review of Intelligent, Secure, and Privacy-Preserving Internet of Vehicles

This study is intended to serve as a structured reference for the development of scalable, trustworthy, and intelligent IoV systems by highlighting state-of-the-art techniques, open research gaps, and future directions.

MA-RL and WOA-Based Secure Routing: A Federated Learning Approach for Trust Evaluation and DoS Attack Mitigation in IoT Networks

The growing size and sophistication of the Internet of Things (IoT) has brought up the issue of network security and routing efficiency, especially in unfriendly settings where Denial-of-Service (DoS) attacks are frtaent. Conventional trust-based routing algorithms are not very flexible and are not capable of adequatel...

HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning

HybridFLow is presented, a closed-loop SDN-driven orchestration framework that integrates network-layer intelligence directly into hybrid FL and generates calibrated per-client communication-time estimates before each training round and uses them to partition clients into synchronous and asynchronous groups while balan...

Beyond Conventional Federated Learning via High-Order Regularization

HiFedProx is introduced, which replaces the quadratic penalty with a scale-matched power-type regularizer indexed by p, and combines this geometry with finite-budget stochastic client optimization and same-minibatch Armijo backtracking.