federated learning

PRIVACY-PRESERVING FEDERATED DEEP LEARNING FOR ZERO-DAY ATTACK DETECTION USING HYBRID CNN–LSTM NETWORKS

The expanding deployment of Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices increases exposure to previously unseen cyberattacks, while centralized intrusion-detection training requires network records from different sites to be pooled. This paper presents a federated convolutional long short-...

Comparative Performance of Federated and Centralized Learning for Brain Tumor Classification and Segmentation

Purpose To compare federated learning (FL) and centralized learning (CL) performance for brain tumor classification and segmentation and examine whether specific configurations confer measurable performance advantages. Materials and Methods In this systematic review and meta-analysis, Medline, EMBASE, PubMed, Web of Sc...

A Privacy-Preserving Data Sharing Framework Driven by Blockchain for Smart Childcare Subsidy Systems

With the continuing development of digital government and public childcare-assistance programs, cross-departmental subsidy services must jointly address sensitive-data minimization, dynamic authorization, fraudulent-application identification, and accountable auditing. This paper proposes a blockchain-driven privacy-pr...

Federated Learning and Privacy-Preserving AI in Healthcare

The healthcare industry collects large volumes of sensitive information every day; however, it shares these data only to a limited extent because of regulatory requirements, competitive concerns, and other constraints. This makes it difficult to develop AI models for cybersecurity applications. The proposed solution is...

Personalized Federated Intrusion Detection for Non-IID IIoT Traffic via DDPG-Guided Adaptive Aggregation

AI-Driven Threat Detection Architecture

The rapid proliferation of digital infrastructure, spanning cloud computing, the Internet of Things (IoT), edge networks, and mobile platforms, has dramatically expanded the attack surface available to cybercriminals. Traditional signature-based intrusion detection systems, while effective against known threats, are fu...

Governance-aware autonomous retail coordination in artificial intelligence cities using multi-agent reinforcement learning, blockchain accountability, and federated learning

When autonomous systems take operational authority over urban commerce, accountability and human oversight matter as much as efficiency. We present AAIRM, a governance-aware autonomous retail coordination framework addressing three requirements for trustworthy procurement: tamper-evident decision provenance, data sover...

Energy-Efficient Offloading, Caching, and Resource Allocation for Blockchain-Assisted Low-Altitude Flying Networks: An Integrated Federated Learning and MAPPO Approach

In 6G low-altitude edge intelligent networks, the proliferation of delay-sensitive Internet of Things (IoT) services exacerbates mutual interference among IoT devices, thereby reducing the efficiency of resource allocation. To address this challenge, we propose a blockchain-assisted federated learning (FL)-based four-l...

FedPKD+: A Prototype-Based Framework for Efficient and Flexible Heterogeneous Federated Learning

Federated Learning (FL) enables collaborative model training across mobile and edge devices without sharing raw data, but its deployment is hindered by <italic>system heterogeneity</italic> and <italic>non-IID</italic> data. Existing FL methods either require homogeneous architectures or suffer from accuracy loss and h...

FedTuneFM: Federated Fine-Tuning of Foundation Models for Mobile Edge Computing via Adaptive Compression and Attention Alignment

Federated Learning (FL) has emerged as a promising paradigm for fine-tuning large-scale Foundation Models (FMs) in distributed environments while preserving data privacy. However, efficiently adapting FMs in FL remains challenging, especially in mobile edge computing scenarios where devices are resource-constrained and...

OFDM-Assisted Over-the-Air Computation With Privacy Protection in Federated Learning

Federated Learning (FL) is an emerging distributed machine learning paradigm that protects data privacy by performing iterative local training and gradient aggregation across multiple devices and a central server. Over-the-air computation enables fast aggregation when multiple devices need to upload their local gradien...

Transmission-Efficient Federated Learning via Chessboard Convolutional Kernel

Reducing the communication cost to alleviate the contradiction between the limited bandwidth and huge transmitted parameters in Federated Learning (FL) is a persisting challenge. Since the transmission parameters obtained by vanilla convolution primarily rely on the product of kernel and channels, recent works (e.g., F...