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 datasets to the central server, raising significant privacy concerns. Our framework addresses this limitation by employing SMPC protocols, enabling model updates to be aggregated securely without revealing the underlying data. This approach provides a strong privacy guarantee, combining the benefits of federated learning with robust privacy protection. We present a detailed description of the system architecture, the SMPC protocols utilized, and the mathematical formulation underpinning the aggregation process. The core claim of this work is the ability to enable collaborative model training across multiple parties without revealing individual data. The core mechanism relies on implementing a federated learning framework based on secure multi-party computation protocols, where model updates are aggregated securely without exposing the underlying data. This research represents a significant advancement in the field of privacy-preserving machine learning.

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 review looks closely at the use of machine learning (ML) methods for predicting trust in online auction settings, focusing on research published from 2005 to 2024. This study covers the full methodological spectrum: classical approaches like logistic regression, support vector machines, and random forests, through to modern deep learning architectures including recurrent networks, graph neural networks, and transformer models. This paper also examined hybrid frameworks that weave together behavioral analytics, NLP-based review analysis, and social network signals. The benchmark datasets the community relies on are discussed, how performance is measured, and the recurring challenges researchers keep running into, such as class imbalance, feature engineering bottlenecks, concept drift, and the cold-start problem are highlighted. The comparative analysis shows that graph-based models using social trust propagation and ensemble methods that combine various feature sets often perform best. Many achieve macro F1 scores above 0.92 on recognized benchmarks. The paper ends with a research roadmap that highlights open problems and new opportunities. These include using federated learning to maintain privacy during trust assessments, integrating large language models, and developing real-time adaptive trust scoring. This work provides a solid reference for researchers and practitioners in machine learning, e-commerce security, and computational trust.

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 system leverages a blockchain network to provide a robust and verifiable mechanism for validating model updates. Each client's update is cryptographically signed and submitted to the blockchain. The blockchain then employs a consensus algorithm to verify the update's validity, ensuring that only legitimate updates are added to the global model. This approach significantly enhances the security and reliability of DFL, particularly in environments with untrusted participants. The core claim is that blockchain-based consensus mechanisms can provide robust BFT for DFL, while the core mechanism involves using a blockchain network to verify and validate model updates. This addresses the security vulnerabilities of traditional DFL by utilizing the inherent properties of blockchain technology. We demonstrate the feasibility and effectiveness of this system through a detailed theoretical analysis and design considerations.

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. Predictive modeling approaches offer potential to guide risk stratification and inform clinical decision-making, but small sample sizes, overfitting, and poor data quality have limited model reproducibility and widespread adoption. As utilization of CAR T-cell therapy broadens, identifying additional biomarkers, developing context-specific models, standardizing guidelines for emerging toxicities, and leveraging federated learning to promote collaborative data sharing will be critical. Significance: Predictive models integrating biomarkers and clinical variables are increasingly used to forecast potential toxicities after CAR T-cell therapy. However, due to heterogeneity in patient populations and cellular therapy products, the rapidly evolving nature of the field, and continued advancements in management of inflammatory toxicities, modeling in CAR T-cell therapy faces significant challenges. This comprehensive review of existing/emerging models serves to delineate components of developing predictive models including discrimination, calibration, biomarker integration, validation, and mitigation of overfitting while highlighting strengths, opportunities for improvement, and future directions applicable to CAR T-cell therapy.

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 traditional differential privacy (DP) mechanisms. This paper introduces a novel differential privacy framework specifically designed for federated learning scenarios with non-IID data. Our approach incorporates adaptive noise scaling, dynamically adjusting the privacy budget based on the measured data heterogeneity among clients. This tailored mechanism mitigates the increased privacy risk associated with non-IID data, providing a more robust and effective solution for privacy-preserving federated learning. We demonstrate the effectiveness of our method through theoretical analysis and highlight its advantages over conventional DP techniques in this context.

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 distributed across multiple entities. Our approach addresses this limitation by enabling each participating entity to simultaneously learn causal relationships within its local data using Bayesian networks. These locally learned models are then aggregated to construct a global causal model, mitigating the need for centralized data sharing. We introduce a specific federated learning architecture tailored for causal discovery, incorporating techniques to address confounding and selection bias. The core of the method lies in the iterative refinement of Bayesian network structures through distributed learning, followed by weighted averaging of the learned parameters. We demonstrate the potential of this method through a theoretical analysis, outlining its advantages in terms of privacy preservation, scalability, and robustness. The resulting global causal model provides insights into the underlying causal relationships while respecting data locality. This approach offers a viable pathway for causal inference in distributed, privacy-sensitive environments.

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 decentralized federated learning system that leverages threshold cryptography and differential privacy to address these concerns. The core claim is that protecting user privacy in federated learning remains a significant challenge, and this system provides a robust solution. The system operates through a series of distributed rounds where participants collaboratively update model parameters using threshold cryptography to ensure secure aggregation and differential privacy to mitigate individual data exposure. This approach eliminates the need for a central server, enhancing both privacy and security. The system is designed for scalability and adaptability, making it suitable for various decentralized data scenarios. This work contributes to the development of more secure and privacy-preserving federated learning solutions.

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 sensors and actuators, leading to communication bottlenecks and potential privacy concerns. This paper proposes a novel framework that integrates federated learning and edge computing to overcome these limitations. The framework enables decentralized model training and updates for the digital twin, reducing communication overhead and preserving data privacy. The core mechanism involves data aggregation and processing at the edge, followed by federated learning to iteratively refine the digital twin model. This approach offers a scalable and efficient solution for infrastructure management, enabling real-time monitoring, predictive maintenance, and optimized operational decisions. The key contributions of this work lie in the synergistic combination of these technologies, delivering a robust and adaptable system for managing complex infrastructure.

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

Complex diseases, including cancer, rare genetic disorders, neurodevelopmental and psychiatric conditions, and neurodegenerative diseases, arise from interactions among genetic variation, gene regulation, and cellular states that are difficult to capture using a single data type or biological scale. Biological foundation models address this challenge by treating nucleotides and genes as tokens and learning representations that can be transferred to downstream biomedical and clinical tasks. In this review, we examine two major model classes, genomic sequence foundation models and cell foundation models, and compare their tokenization strategies, model architectures, pretraining objectives, and adaptation methods. We summarize their emerging applications in regulatory variant interpretation, disease-associated cell-state analysis, drug-response prediction, and therapeutic target discovery across complex diseases. We distinguish applications supported by experimental or retrospective validation from those that remain primarily computational or conceptual. We further discuss key challenges to clinical translation, including multimodal data integration, model interpretability, benchmarking, patient-specific prediction, and privacy protection. We highlight future opportunities to integrate biological foundation models with emerging frameworks of medical digital twins, agentic AI, and federated learning. By linking model design to translational goals, this review provides a practical framework for evaluating biological foundation models and their readiness for complex disease research and clinical use.

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 information-theoretic framework for quantifying trust in FL, moving away from traditional subjective trust assumptions. We define a "trust score" based on the mutual information between local model updates and a global consensus model. This score directly measures the contribution of each client's update to the collective knowledge, providing a rigorous metric for assessing model divergence and identifying potential outliers. The core contribution of this paper lies in providing a quantifiable and objective method for evaluating trust in FL, enabling more robust and reliable deployments. We explore the theoretical properties of this mutual information-based trust score and demonstrate its potential for improved outlier detection and model convergence in federated learning scenarios. The framework utilizes concepts from information theory, specifically mutual information, to provide a mathematically grounded approach to assessing the quality and trustworthiness of decentralized model training.