federated learning

FedHC-GNN for Federated Multimodal Representation Learning of Cross-Modal Disclosure Consistency in Online Charity Transparency Assessment

Privacy-Preserving Intrusion Detection via Federated Learning: An Empirical Evaluation with Automated Response Recommendation

Distributed Federated Learning with Byzantine Fault Tolerance using Homomorphic Encryption

Federated learning (FL) offers a promising approach to training machine learning models across decentralized devices while preserving data privacy. However, FL systems are vulnerable to Byzantine faults, where malicious participants introduce corrupted model updates, compromising the integrity of the global model. This...

Distributed Learning with Federated Generative Adversarial Networks

This paper presents a novel framework for distributed learning of Generative Adversarial Networks (GANs) through a federated learning approach. Traditional GAN training demands centralized computation and large-scale datasets, posing significant challenges for privacy-sensitive and resource-constrained environments. Th...

Algorithmic Fairness via Randomized Smoothing in Federated Learning

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent data heterogeneity and potential biases present in these datasets pose significant challenges to achieving algorithmic fairness. This paper invest...

"Processed Results for Low-Visible Robust Federated Learning"

"This dataset contains the processed experimental results and figure-source files associated with the study \u201cLow-Visible Robust Weight Generation for Byzantine Federated Learning via Sketch-Based Detection.\u201d The data support the evaluation of CipherSketch-RFL under Byzantine model-update attacks, including si...

Computational Heterogeneous-Response Pharmacology (CHRP): A Formalized Framework for Embodied Pathology-Guided Individualized Drug Verification 计算异质性响应药理学验证理论(CHRP):基于具身病理学思想的个体用药显性验证形式化框架

Contemporary pharmacology remains anchored to a population-centric epistemology, wherein the "average patient" derived from randomized controlled trials (RCTs) serves as the gold standard for dosing and therapeutic decision-making. This framework systematically marginalizes individual heterogeneity in drug response, tr...

Scalable Federated Learning with Differential Privacy and Graph-Based Communication

Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the data itself. However, traditional FL approaches often suffer from significant communication overhead and potential privacy vulnerabilities. This research proposes a nove...

Adaptive Differential Privacy for Federated Learning via Randomized Compression

Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, achieving strong differential privacy (DP) guarantees in FL is a significant challenge, particularly when combined with compression techniques commonly employ...

Scalable Federated Learning with Differential Privacy and Graph-Based Communication

Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the data itself. However, traditional FL approaches often suffer from significant communication overhead and potential privacy vulnerabilities. This research proposes a nove...

Distributed Federated Learning with Differential Privacy for Personalized Models

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...

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...