federated learning

Hybrid Distributed Learning With Knowledge Distillation for Resource-Efficient Intrusion Detection in Distributed Networks

A novel AI-driven distributed NIDS that considers the computing capabilities of resource-constrained nodes while enabling efficient learning in distributed environments is proposed and can achieve accuracy comparable to a centralized model while reducing local computational overhead and maintaining stable convergence u...

Twin2Twin: Digital Twin–Driven Orchestration of Federated Learning in Medical IoT Environments

Sixth-generation (6G) networks are expected to integrate intelligence as a native capability, enabling advanced verticals such as digital health (eHealth) supported by large-scale Internet of Medical Things (IoMT) deployments. In this context, Federated Learning (FL) is emerging as a promising paradigm for collaborativ...

HBA: Hijacking-Based Backdoor Attack for Vertical Federated Learning

Vertical federated learning (VFL) is a distributed machine learning paradigm designed for scenarios with vertically partitioned data features, making it highly compatible with Internet of Things (IoT) ecosystems. While promoting collaborative modeling among IoT devices, VFL also introduces new security risks, particula...

FedProIn: Mitigating Client Drift for Learnable Prototypes in Federated Medical Imaging

This work proposes a prototype-based, influence-aware federated learning framework (FedProIn) that uses multiple learnable class prototypes to capture shared semantic structures across heterogeneous clients and introduces feature divergence loss and prototype contrastive loss to mitigate client drift by decomposing it...

RCFL: Recursive Clustered Federated Learning for Distributed Concept Drift

FedJoint: A software architecture for adaptive orchestration in federated learning systems

Privacy-Preserving Federated Multimodal Learning for Healthcare Applications

PPTRecS-FL: Privacy-preserving task recommendation strategy based on federated learning in mobile crowdsensing

FedOut: Federated learning with outsourced homomorphic encryption for resource-constrained IoT devices

Adversarial Machine Learning: Security Risks and Defense Strategies in AI-Driven Applications

A detailed overview of the security risks associated with adversarial attacks is offered, including evasion attacks carried out at inference time, data poisoning that corrupts the training process, backdoor insertion that hides dormant triggers inside a model, and model inversion that leaks private information back out...

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms

ProphDR is an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism, and generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes consistent with established mechanisms in N...

Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.

The first comprehensive benchmarking framework specifically designed to accommodate inter-dataset heterogeneity is presented, finding that well-designed small datasets can match or even surpass the performance of larger benchmarks, suggesting that different metrics are applicable to different datasets/testing scenarios...