federated learning

Unlocking the Potential of Asynchronous Federated Learning in Non-IID Edge Environments

Asynchronous Federated Learning (AFL) enhances the efficiency of edge collaborative learning systems by asynchronously aggregating client updates to prevent slowdowns from slow clients. However, in non-IID scenarios, such operations magnify uneven learning among various local data, degrading global model generalization...

FedNereus: Incentive-Aware Heterogeneous Federated Learning Under Resource Constraint

As public concern about data privacy continues to grow, federated learning (FL), as a machine learning technique that does not require the disclosure of user raw data, has attracted widespread attention. However, it still has many common problems. The first problem stems from the absence of incentive mechanisms utilize...

AsyncFed-SAR: Toward Communication-Efficient Asynchronous Federated Learning for SAR Image Target Recognition

BioMutFed+: Mutation-Driven Federated Learning for IIoT

The evolution towards Industry 5.0 underscores the critical need for privacy-preserving and human-centric technologies within Industrial Internet of Things (IIoT) ecosystems. Federated Learning (FL), an essential framework enabling decentralized model training while safeguarding data privacy, continues to encounter sig...

CODEA: Online Container Deployment and Edge Association for Multi-Edge Federated Synergy Learning

Federated edge learning (FEEL) is a prospective paradigm enabling edge devices to collaboratively participate in machine learning model training, unlocking countless opportunities for edge intelligence. As an extension of FEEL, federated synergy learning (FSyL) alleviates the computation and communication burdens on re...

A Bio-Inspired Hierarchical Federated Learning Framework With Joint Computing Communication Control Optimization for Green UAV Inspection

With the development of UAV communications and aerial edge intelligence, Uncrewed Aerial Vehicles (UAVs) are increasingly used for inspection in high-risk environments. However, limited onboard energy and data-privacy constraints make efficient collaborative learning challenging. Hierarchical Federated Learning (HFL) p...

Fed-MoSeC: A Federated Learning Framework for Cross-Modal Semantic Communication Systems in Mobile Networks

Semantic communication systems in mobile networks necessitate real-time collaborative updates of semantic encoders as a result of node mobility that induces semantic extraction drift. However, diversity within modal transmission content and heterogeneous encoder architectures, along with challenges such as imbalanced t...

"PRESAGE: Physics-Anchored Digital Twin of a Hot Strip Mill -Two-Year Dataset from Controlled Deployment (April 2024-March 2026)"

"This dataset comprises 6.99 million evaluated passes across 583,468 coils over 24 months (April 2024\u2013March 2026) from a 12-stand hot strip mill operating under controlled conditions. It contains comprehensive pass-level records with 56 columns spanning full PLC signal integration (444 tags), per-coil summaries, r...

Wenrui-Yu/hssp_dfl_sa: v1.0

Code for paper "When Topology Betrays Privacy: Lattice-Based Reconstruction Attacks on Secure Aggregation in Decentralized Federated Learning"

Wenrui-Yu/hssp_dfl_sa: v1.0

Code for paper "When Topology Betrays Privacy: Lattice-Based Reconstruction Attacks on Secure Aggregation in Decentralized Federated Learning"

Federated Learning as a Privacy-Enhancing Framework: Risks, Protection Mechanisms, and Applications

This paper argues that federated learning should be evaluated as a privacy-enhancing framework rather than as a complete privacy guarantee, and proposes a comparat ive framework that asks what federated learning protects, what it still exposes, and which auxiliary mechanisms are needed in different application settings...

Hybrid Machine-Learning Based Detection for Hospital Networks Threat Mitigations

The study concluded that hybrid machine learning significantly improves hospital intrusion detection performance compared to traditional systems and should be integrated into larger healthcare datasets for broader scalability across different environments.