AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Federated learning (FL) offers a promising approach to distributed machine learning by enabling collaborative model training without directly exchanging raw data. However, traditional FL systems are plagued by privacy vulnerabilities and significant communication costs. This paper proposes a novel framework, Quantum Wa...
This paper proposes a novel approach to network anomaly detection leveraging the strengths of federated learning and graph neural networks (GNNs). Traditional centralized anomaly detection systems suffer from single points of failure and privacy concerns. Our decentralized system addresses these limitations by enabling...
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...
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 (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 –...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, this approach inherently exposes sensitive user data, raising significant privacy concerns. This paper proposes a novel framework for distributed differential...
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