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2,119 papers
Federated learning (FL) presents a promising approach to training machine learning models across decentralized devices while preserving data privacy. However, traditional FL methods often lack sufficient privacy guarantees, leaving users vulnerable to privacy breaches. This work proposes a novel framework for distribut...
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...
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...
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...
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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...
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...
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...
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...
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...
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...
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.
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...
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
A Blog post by IBM Research on Hugging Face
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
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