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federated learning

2,118 papers

#federated learning Open access Sep 2026

Decentralized Federated Learning with Byzantine Fault Tolerance using Blockchain Verification

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can intentionally corrupt the learning process by submitting...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Distributed Bayesian Optimization with Federated Learning

This paper proposes a novel distributed Bayesian optimization framework utilizing federated learning to optimize black-box functions across multiple clients while preserving data privacy. The core concept involves each client independently training a local Bayesian model based on its local dataset. Subsequently, a cent...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Adversarial Robustness Certification for Federated Learning via Randomized Smoothing

Federated learning (FL) offers a promising paradigm for training machine learning models across decentralized devices while preserving data privacy. However, recent research has demonstrated that FL systems are susceptible to adversarial attacks, where maliciously crafted inputs can significantly degrade model performa...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Decentralized Federated Learning with Byzantine Fault Tolerance using Blockchain Verification

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can intentionally corrupt the learning process by submitting...

Jincheng Zhang · 0 citations
#federated learning Dataset Open access Sep 2026

Histogram-Optimized CKKS Encrypted Federated XGBoost for Privacy-Preserving Healthcare Analytics

This repository contains the dataset associated with the manuscript entitled “Histogram-Optimized CKKS Encrypted Federated XGBoost for Privacy-Preserving Healthcare Analytics.” The experiments use two datasets: A fully reproducible synthetic EHR dataset. The publicly available UCI Heart Disease dataset ( Janosi, A., St...

Vijayalakshmi · 0 citations
#federated learning Book Sep 2026

Securing Supply Chain Data Integrity

The digitization of global supply chains has enhanced efficiency but simultaneously expanded the attack surface for cyber-sabotage and insider manipulation. Ensuring data integrity across interconnected entities has become a strategic necessity for operational resilience. This chapter presents an AI-driven multi-layere...

Syed Mohd Faisal, Wasim Khan, Mohammad Ishrat · 0 citations
#federated learning Open access Sep 2026

Decentralized Federated Learning with Differential Privacy for Sensor Networks

Decentralized Federated Learning (DFL) offers a promising approach to machine learning in resource-constrained environments like sensor networks, where data resides locally and communication is limited. However, traditional federated learning paradigms often fall short in providing robust privacy guarantees and are sus...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Predictive Maintenance using Federated Learning with Edge-Based Anomaly Detection

Predictive maintenance aims to anticipate equipment failures, minimizing downtime and maintenance costs. Traditional approaches often rely on centralized data collection, raising significant privacy concerns, particularly when dealing with sensitive sensor data from industrial equipment. This research proposes a novel...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Development Of a Federated Learning Model for Fair Healthcare Data Analysis in Selected African Countries

Federated learning (FL) offers a privacy-preserving approach to collaborative machine learning by enabling model training across decentralised health datasets without sharing sensitive individual-level data. This study investigated the fairness and reliability of FL for predicting antenatal care (ANC) utilisation in lo...

Prosper Ukachi · 0 citations
#federated learning Open access Sep 2026

Privacy-preserving intrusion detection in IoT smart homes using a federated hybrid 1D-CNN–LSTM model with explainable AI

FedShield-IDS is proposed, a novel federated intrusion detection framework that integrates a hybrid one-dimensional Convolutional Neural Network with Long Short-Term Memory units to simultaneously capture spatial traffic fingerprints and long-range temporal attack dynamics across IoT edge devices.

Ghada Abdelhady, Karim Wael Hussein, Islam Anwar Ali Gad · 0 citations
#federated learning Open access Sep 2026

Development Of a Federated Learning Model for Fair Healthcare Data Analysis in Selected African Countries

Federated learning (FL) offers a privacy-preserving approach to collaborative machine learning by enabling model training across decentralised health datasets without sharing sensitive individual-level data. This study investigated the fairness and reliability of FL for predicting antenatal care (ANC) utilisation in lo...

Prosper Ukachi · 0 citations
#federated learning Open access Sep 2026

Decentralized Federated Reinforcement Learning with Byzantine Agreement

Decentralized Federated Reinforcement Learning (DFRL) presents a promising approach to training robust and adaptable RL agents by leveraging distributed data and computational resources. However, this paradigm is susceptible to attacks from Byzantine actors who can inject malicious updates, jeopardizing the learning pr...

Jincheng Zhang · 0 citations

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MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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