federated learning

ZTAAC-FL: A Zero Trust Adaptive Access Control Framework for Robust Federated Learning in the Industrial Internet of Things

A Verifiable Momentum Federated Learning Scheme

VMFL employs a chaotic map to generate a pseudo-random sequence and embeds the sum of local momentum vectors as verification data, constructing a verifiable momentum protected by additively homomorphic encryption, and designs an aggregation verification protocol that verifies the aggregated momentum by checking vector-...

AI-Driven Big Data Classification: A Taxonomy of Learning Paradigms, Scalability Approaches, and Emerging Research Directions

A taxonomy of existing methods into supervised and instance-based learning, deep and hybrid architectures, and imbalance-aware learning based on resampling is proposed, and a research-gap table identifies open issues in interpretability, cross-domain transfer, and energy-aware computation.

Federated Learning for Histopathological Image Segmentation: A Privacy-Preserving Study Using the EBHI-Seg Dataset

Federated learning (FL) enables collaborative model training without centralizing sensitive medical data, making it attractive for digital pathology. This study evaluates FL for histopathological image segmentation using the EBHI-Seg dataset under independent and identically distributed (IID) and classstratified non-II...

GuardML: Enabling Secure and Accurate Federated Learning for Medical Diagnostics Using Hybrid Homomorphic Encryption

RetFL: a privacy-preserving and traceable framework for robust federated learning

RetFL is proposed, a CKKS-enabled robust aggregation framework for DFL that establishes a decentralized training workflow with VRF-based candidate selection and view change, and designs a weighted aggregation scheme that incorporates cosine similarity and a dynamic reputation mechanism to weight updates and suppress pe...

Retrieval-Guided Semi-supervised Federated Learning for Medical Image Segmentation

Distributed Optimization Under Uncertainty: A Cross-Paradigm Review and Future Directions

Key findings include the widespread adaptation of classical algorithms, particularly the alternating direction method of multipliers (ADMM), across all uncertainty paradigms, alongside an evolution toward data-driven and adaptive methods for real-time uncertainty handling.

FuzzyCS-Proto: fuzzy client selection with class-wise prototype learning for federated ship underwater structural surface condition classification

Underwater structural inspection is essential for ship maintenance and marine infrastructure management, but practical inspection data are often distributed across different vessels, ports, inspection platforms and sensing devices. Direct centralized training may be limited by data ownership, communication cost, and op...

ZTAAC-FL: A Zero Trust Adaptive Access Control Framework for Robust Federated Learning in the Industrial Internet of Things

ASHA: Adaptive Scalable Hybrid Algorithm for federated learning

Mixture-of-prompt experts for federated sentiment classification under non-IID data

Federated learning (FL) enables collaborative text classification while preserving data privacy, yet its performance can be degraded under non-IID data. This challenge is particularly severe in natural language processing, where client data may differ in linguistic style, topic distribution, and label semantics. Recent...