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2,117 papers
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data while preserving data privacy. However, existing privacy-preserving techniques frequently rely on approximations and heuristics, often lacking rigorous mathematical justification. This work presents a novel inf...
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data without direct data sharing. However, the traditional approach of using static homomorphic encryption (HE) schemes for FL introduces significant overheads, particularly when combined with the iterative nature...
This paper presents a novel approach to decentralized knowledge graph construction using Bayesian Networks and Federated Learning. The core idea is to distribute the knowledge graph construction process across a network of nodes, each maintaining a local Bayesian Network. Federated Learning is then employed to iterativ...
Custom code for the manuscript "Direction-shaped anisotropic pre-DP gradient perturbation and clinically grounded gradient-inversion evaluation for differentially private federated medical imaging." Implements a three-tier federated learning pipeline (FedProx + Opacus DP-SGD + a pre-DP perturbation shield) and evaluate...
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Custom code for the manuscript "Direction-shaped anisotropic pre-DP gradient perturbation and clinically grounded gradient-inversion evaluation for differentially private federated medical imaging." Implements a three-tier federated learning pipeline (FedProx + Opacus DP-SGD + a pre-DP perturbation shield) and evaluate...
Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, the inherent trust relationships within FL systems are complex and often overlooked. This paper presents a formal model for analyzing trust dynamics in FL, incorpor...
This paper investigates the application of federated learning and differential privacy for decentralized anomaly detection in network traffic. Traditional anomaly detection methods often rely on centralized data collection, posing significant privacy risks and hindering scalability. This research proposes a novel frame...
This paper proposes a novel approach to distributed learning that leverages the principles of Federated Byzantine Fault Tolerance (FBT) to achieve robustness and scalability against malicious actors. Traditional federated learning systems are vulnerable to attacks where adversaries can manipulate the global model by in...
This paper explores the integration of differential privacy (DP) with federated learning (FL) to facilitate personalized healthcare applications while rigorously safeguarding patient privacy. Traditional machine learning approaches relying on centralized data collection pose significant risks to individual confidential...
This paper presents a novel approach to enhancing data privacy in federated learning by integrating differential privacy with homomorphic encryption. Federated learning, a distributed machine learning paradigm, allows training models across multiple devices without directly sharing raw data. However, this approach stil...
Federated learning offers a promising architecture for training clinical machine learning models across distributed Ugandan health facilities without centralising sensitive patient data. However, the practical viability of this approach depends on its robustness to two conditions that characterise real-world health inf...
Federated learning (FL) has emerged as a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent vulnerability of current FL protocols to Byzantine attacks – where malicious nodes inject false information to corrupt the global model –...
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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