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Tag: federated

  • Federated Measurement of Demographic Disparities from Quantile Sketches

    Federated Measurement of Demographic Disparities from Quantile Sketches arXiv:2602.18870v1 Announce Type: new Abstract: Many fairness goals are defined at a population level that misaligns with siloed data collection, which remains unsharable due to privacy regulations. Horizontal federated learning (FL) enables collaborative modeling across clients with aligned features without sharing raw data. We study federated auditing…

  • Federated Learning, Part 2: Implementation with the Flower Framework 🌼

    Federated Learning, Part 2: Implementation with the Flower Framework 🌼 Implementing cross-silo federated learning step by step The post Federated Learning, Part 2: Implementation with the Flower Framework 🌼 appeared first on Towards Data Science. Parul Pandey Go to original source

  • Communication-Efficient Federated Risk Difference Estimation for Time-to-Event Clinical Outcomes

    Communication-Efficient Federated Risk Difference Estimation for Time-to-Event Clinical Outcomes arXiv:2601.14609v1 Announce Type: new Abstract: Privacy-preserving model co-training in medical research is often hindered by server-dependent architectures incompatible with protected hospital data systems and by the predominant focus on relative effect measures (hazard ratios) which lack clinical interpretability for absolute survival risk assessment. We propose FedRD,…

  • Federated Learning, Part 1: The Basics of Training Models Where the Data Lives

    Federated Learning, Part 1: The Basics of Training Models Where the Data Lives Understanding the foundations of federated learning The post Federated Learning, Part 1: The Basics of Training Models Where the Data Lives appeared first on Towards Data Science. Parul Pandey Go to original source

  • Federated Learning and Custom Aggregation Schemes

    Federated Learning and Custom Aggregation Schemes A practical guide to designing and analyzing robust aggregation strategies The post Federated Learning and Custom Aggregation Schemes appeared first on Towards Data Science. Salman Toor Go to original source

  • Federated Online Learning for Heterogeneous Multisource Streaming Data

    Federated Online Learning for Heterogeneous Multisource Streaming Data arXiv:2508.06652v1 Announce Type: new Abstract: Federated learning has emerged as an essential paradigm for distributed multi-source data analysis under privacy concerns. Most existing federated learning methods focus on the “static” datasets. However, in many real-world applications, data arrive continuously over time, forming streaming datasets. This introduces additional…

  • Algorithm Protection in the Context of Federated Learning 

    Algorithm Protection in the Context of Federated Learning  While working at a biotech company, we aim to advance ML & AI Algorithms to enable, for example, brain lesion segmentation to be executed at the hospital/clinic location where patient data resides, so it is processed in a secure manner. This, in essence, is guaranteed by federated…

  • Online federated learning framework for classification

    Online federated learning framework for classification arXiv:2503.15210v1 Announce Type: new Abstract: In this paper, we develop a novel online federated learning framework for classification, designed to handle streaming data from multiple clients while ensuring data privacy and computational efficiency. Our method leverages the generalized distance-weighted discriminant technique, making it robust to both homogeneous and heterogeneous…

  • Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics

    Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics arXiv:2503.09649v1 Announce Type: cross Abstract: Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. As the evolution of biobanks in genetics and systems biology has proved, accessing more extensive and varied data pools leads…

  • Federated Variational Inference for Bayesian Mixture Models

    Federated Variational Inference for Bayesian Mixture Models arXiv:2502.12684v1 Announce Type: new Abstract: We present a federated learning approach for Bayesian model-based clustering of large-scale binary and categorical datasets. We introduce a principled ‘divide and conquer’ inference procedure using variational inference with local merge and delete moves within batches of the data in parallel, followed by…