federated learning

Budget-Prioritized Dynamic Regrouping for Edge Federated Services Under Workload Drift and Privacy-Budget Constraints

Federated learning enables model training in edge and distributed service environments without directly sharing raw data. In long-running edge federated services, fixed collaboration structures may become inefficient under workload drift, whereas frequent regrouping can incur migration overhead, group churn, and additi...

MAPFL: Modular Adaptive Personalised Federated Learning From Visual Sentiment to Physical Activity

Human‐centric sensing tasks such as visual sentiment analysis and smartphone‐based human activity recognition (HAR) exhibit strong client heterogeneity from feature, label, and concept shifts. Among these, concept shift—how individual users interpret and generate human‐centric signals—is particularly challenging yet...

Robust Federated Learning for Detecting False Data Injection Attacks in Advanced Metering Infrastructure under Non-IID Data — Source Code

This repository contains the complete source code for the paper "Robust Federated Learning for Detecting False Data Injection Attacks in Advanced Metering Infrastructure under Non-IID Data." The framework couples a lightweight two-branch GRU local detector with a server-side robust aggregation pipeline (per-client EMA...

Distributed Learning of Neural Networks with Federated Learning and Differential Privacy

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, FL systems often struggle with privacy concerns and convergence issues, particularly when dealing with heterogeneous data distributions across clients. This pa...

A personalized federated deep learning framework predicts student performance in large-scale examinations across heterogeneous regional distributions

The findings indicate that regionally adaptive models can support targeted resource allocation, while cautioning that systems predicting attainment from environmental context risk formalising the disadvantage they measure.

Robust Federated Learning for Detecting False Data Injection Attacks in Advanced Metering Infrastructure under Non-IID Data — Source Code

This repository contains the complete source code for the paper "Robust Federated Learning for Detecting False Data Injection Attacks in Advanced Metering Infrastructure under Non-IID Data." The framework couples a lightweight two-branch GRU local detector with a server-side robust aggregation pipeline (per-client EMA...

PhysGuard-FL: Reproducibility package for physics-aware Byzantine-robust federated learning in chemical process monitoring

Complete research compendium for PhysGuard-FL, a physics-aware Byzantine-robust federated-learning framework evaluated for chemical-process monitoring. The frozen benchmark uses six Tennessee Eastman Process operating modes, 20 federated clients, 20 communication rounds, five local steps, and 10 matched random seeds. T...

Privacy-Preserving Federated Learning for Cross-Border Data Flows in Uganda: Robustness Under Distribution Shift and Sparse Labels

Cross-border data flows are central to Uganda's digital economy, yet their governance is complicated by competing pressures: the need for international integration and the legal obligation to protect citizen privacy. Federated learning offers a promising architecture for training machine learning models across distribu...

MAFL-PdM: A Multi-Agent Federated Learning Framework for Personalised Predictive Maintenance Across Heterogeneous Industrial IoT Sites

A Multi-Layer Auditable Vertical Federated Learning Prototype for Power Equipment Supply Chains: Reproducibility, Robustness, and Privacy-Boundary Evaluation

Transformer lifecycle data across organizations are typically vertically partitioned among material suppliers, manufacturers, logistics service providers, testing agencies, and operation and maintenance units. This study presents a reproducible multi-layer vertical federated learning (VFL) prototype that integrates sal...

Privacy-Preserving Federated Learning for Cryptography Cybersecurity in Uganda: Robustness Under Distribution Shift and Sparse Labels

The federated monitoring of cryptographic failure and cyber-intrusion signals across Ugandan institutions inherits two under-addressed tensions: client event distributions drift with sectoral traffic, software configuration and adversary behaviour, and attack labels remain sparse because cryptographic incident confirma...

Decentralized Vertical Federated Learning via Graph-Based Learning Consensus