federated learning

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...

Federated predictive load balancing for adaptive resource management in fog computing

A Federated Predictive Predictive Load Balancing (FPLB) framework is proposed to combine Long Short-Term Memory (LSTM) workload forecasting with federated learning, which does not require fog nodes to share their operational data.

IoT-Based Elderly Health Monitoring Information System with Federated Learning for Early Health Risk Prediction

The increasing elderly population in Indonesia has contributed to a higher prevalence of non-communicable diseases, including hypertension, cardiovascular disease, diabetes mellitus, and chronic obstructive pulmonary disease (COPD). This trend highlights the need for continuous, real-time health monitoring rather than...

Smart Cities AVNs: Transparent and Privacy-Preserving Cyber Attack Prediction with Explainable Federated Feature Selection

Differential Privacy for Federated Learning with Dynamic Group Membership

Federated learning (FL) offers a promising approach to machine learning by enabling collaborative model training across decentralized devices without directly exchanging data. However, traditional FL methods often struggle to provide strong differential privacy guarantees, especially when group membership and data hete...

A robust federated learning and deep learning based cyberattack detection algorithm

Distributed Denial-of-Service (DDoS) attacks represent a rapidly growing threat to modern network infrastructures, prompting the development of numerous detection techniques aimed at mitigating their adverse effects. Although Machine Learning (ML) and Deep Learning (DL) approaches have demonstrated strong effectiveness...

Decentralized Federated Learning with Differential Privacy via Graph Consensus

This paper presents a novel approach to decentralized federated learning that leverages graph consensus algorithms to achieve robust and privacy-preserving model aggregation. The core idea is to represent participating devices as nodes within a graph and utilize graph consensus protocols for model update exchange and a...

The Role of AI in Municipal Wastewater Treatment and Management: A Comprehensive Review

This review considers how machine learning (ML), deep learning (DL), evolutionary optimization, reinforcement learning, and hybrid AI–mechanistic models can be utilized in municipal wastewater processes.

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...

AN ADAPTIVE FEDERATED DEEP LEARNING FRAMEWORK FOR SECURE AND PRIVACY PRESERVING PRECISION AGRICULTURE IN DISTRIBUTED IOT NETWORKS | Kashf Journal of Multidisciplinary Research

Distributed Federated Learning with Byzantine Resilience via Secure Multi-Party Computation

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data, thereby preserving user privacy. However, FL systems are vulnerable to Byzantine attacks, where malicious participants can inject corrupted model updates to compromise...

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...