federated learning

A Spatio-Temporal Transformer-Based Approach for Network Attack Detection

With the proliferation of dynamic network environments such as the Internet of Things (IoT) and Vehicle-to-Everything (V2X) networks, challenges related to data localization and privacy leakage have become increasingly prominent. Efficient and privacy-preserving real-time network attack detection has emerged as a criti...

Secure Healthcare Data Analytics for Early Disease Detection: Cybersecurity and Big Data Solutions

This article looks at secure methods for healthcare data analytics leveraging big data technologies in conjunction with advanced cybersecurity methods, including encryption, blockchain, federated learning, and artificial intelligence (AI) models which help ensure patient data remain protected while providing predictive...

VCFedAdam: A Verifiable and Compressed FedAdam Aggregation Scheme for Federated Learning in Distributed Sensor Networks

Experimental results show that VCFedAdam reduces computation overhead by 90.25% and communication overhead by 75.38% compared with traditional secure aggregation, and security analysis confirms that VCFedAdam achieves both privacy protection and aggregation verifiability.

Robust detection method for adversarial backdoor attacks under federated learning architecture

A robust detection method featuring a two layer "norm verification + directional consistency" filtering mechanism combined with the Isolation Forest algorithm to remove malicious outliers, a weighted adaptive robust aggregation that dynamically allocates weights based on client accuracy and consistency, updates the glo...

"Buffalo and Cattle Characteristics Census Survey Dataset 2026"

"Buffalo and Cattle Characteristics Census Survey Dataset 2026 is a synthetic livestock research dataset containing 500 animal-level records and 79 attributes representing buffalo and cattle characteristics relevant to livestock management and animal husbandry research. The dataset incorporates geographical, breed, dem...

Human-centered design based on federated learning to optimize user experience on blockchain platforms

In modern decentralized environments, designing systems that are not only efficient and secure but also understandable and trustworthy for end-users has emerged as the central challenge. Blockchain and Federated Learning (FL) have become two seminal technologies that facilitate privacy-preserving and transparent proces...

Privacy-Preserving Federated Learning for Industrial Supply Chains via Dynamic Masking and Quality-Aware Secure Aggregation

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FAIRVAR: Fair Federated Learning via Variance Regularization

Replication package for "How Technical Architecture Shapes Platform Emergence: A Multi-Agent Simulation of Data Collaboration Strategies"

Replication package for the HICSS-60 paper "How Technical Architecture Shapes Platform Emergence: A Multi-Agent Simulation of Data Collaboration Strategies" (Aoki & Yoshioka-Kobayashi, 2027). The paper formalises self-reliance, raw-data pooling and federated learning as profiles over two architectural primitives — inte...

Replication package for "How Technical Architecture Shapes Platform Emergence: A Multi-Agent Simulation of Data Collaboration Strategies"

Replication package for the HICSS-60 paper "How Technical Architecture Shapes Platform Emergence: A Multi-Agent Simulation of Data Collaboration Strategies" (Aoki & Yoshioka-Kobayashi, 2027). The paper formalises self-reliance, raw-data pooling and federated learning as profiles over two architectural primitives — inte...

Communication-Efficient Federated Learning Based on a Two-Stage Adaptive Basis Matrix for Gradient Compression

Federated learning (FL) faces significant communication overhead due to the repeated exchange of large gradient tensors between clients and the server. While existing compression techniques, such as sparsification and quantization, help reduce this cost, they often result in information loss, which can negatively impac...

Transformer-Enhanced Task Offloading With Privacy Preservation in Heterogeneous and Dynamic Edge Networks

The exponential growth of edge devices and growing demand for low-latency, high-throughput applications have established edge computing as essential infrastructure. Edge computing enables near-data computation to alleviate device burdens, yet task offloading optimization remains challenging due to the heterogeneous, dy...