federated learning

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

A dynamic regrouping problem that jointly captures workload pressure, remaining privacy budget, service utility, and regrouping cost is formulated and Budget-Prioritized Dynamic Regrouping (BP-DR) is proposed, a triggered local method that evaluates single-node candidate operations and commits at most one regrouping op...

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.