federated learning

Decentralized Federated Learning with Secure Multi-Party Computation

This paper proposes a novel approach to decentralized federated learning (DFL) that leverages secure multi-party computation (SMPC) to guarantee data privacy during collaborative model training. Traditional federated learning methods, while promoting data sharing for model improvement, inherently expose individual data...

Machine Learning Approaches for Trust Prediction in Online Auction Platforms: A Comprehensive Review

Online auction platforms have grown enormously over the past two decades, yet trust between participants remains a serious problem. Fraud, shill bidding, and misleading seller behavior keep undermining buyer confidence, and the need for reliable, automated ways to assess trustworthiness has never been greater. This...

Decentralized Federated Learning with Byzantine Fault Tolerance using Blockchain Consensus

This paper proposes a novel approach to decentralized federated learning (DFL) that incorporates Byzantine fault tolerance (BFT) using blockchain consensus. Traditional DFL systems are vulnerable to malicious clients injecting poisoned model updates, compromising the overall model accuracy and data integrity. Our syste...

Data from Predictive Models for Toxicities after CAR T-cell Therapy: Challenges and Opportunities

Abstract Chimeric antigen receptor (CAR) T-cell therapy is increasingly utilized with expanding indications beyond hematologic malignancies. Here, we review existing models developed for predicting toxicities in the CAR T-cell setting and identify both strengths and challenges emerging with their application. Predictiv...

Correction: Hierarchical federated edge learning with criticality-aware aggregation for privacy-preserving fetal health classification

Differential Privacy for Federated Learning with Non-IID Data

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent non-IID (independent and identically distributed) nature of data across clients poses a significant threat to the privacy guarantees provided by t...

Distributed Causal Inference using Federated Learning and Bayesian Networks

This paper proposes a novel framework for distributed causal inference by integrating federated learning with Bayesian networks. Traditional causal inference methods often require centralized data access, posing significant privacy concerns and logistical challenges, particularly in scenarios involving sensitive data d...

Decentralized Federated Learning with Differential Privacy using Threshold Cryptography

Federated learning (FL) presents a promising approach to training machine learning models on decentralized data sources while preserving user privacy. However, traditional FL methods often rely on a central server, introducing a single point of failure and potential privacy risks. This paper proposes a novel decentrali...

Digital Twins for Critical Infrastructure Management using Federated Learning and Edge Computing

The creation and maintenance of digital twins for critical infrastructure systems, such as power grids, water distribution networks, and transportation systems, presents significant computational challenges. Traditional centralized approaches struggle to handle the massive amounts of data generated by distributed senso...

Peer Review Report For: Range-risk estimation for electric vehicles in low-coverage mountain road segments: A digital-twin study with prequential beacon-based Federated Learning [version 1; peer review: 2 approved with reservations]

Biological Foundation Models for Complex Disease Research and Clinical Translation

Biological foundation models are new artificial intelligence tools that can learn useful patterns from large biological datasets and apply them to different research questions to help researchers understand disease mechanisms, identify important genetic changes, predict drug responses, and discover treatment targets.

Information-Theoretic Foundation for Trustworthy Federated Learning

Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data while preserving data privacy. However, the inherent heterogeneity of data across clients and potential malicious behavior introduce challenges to the trustworthiness of FL systems. This work proposes a novel i...