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federated learning

2,118 papers

#federated learning Open access Sep 2026

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...

Yi He · 0 citations
#federated learning Open access Sep 2026

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...

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

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...

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

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...

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

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...

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

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

Training Without Gathering the Data: A State of the Art Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...

Zen Revista, 10 IA · 0 citations
#federated learning Open access Sep 2026

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...

Yi He · 0 citations
#federated learning Open access Sep 2026

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...

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

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...

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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