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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2405.19000 |
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| author | Zhang, Fan Kreuter, Daniel Esteve-Yagüe, Carlos Dittmer, Sören Fernandez-Marques, Javier Ip, Samantha Consortium, BloodCounts! de Wit, Norbert C. J. Wood, Angela Rudd, James HF Lane, Nicholas Gleadall, Nicholas S Schönlieb, Carola-Bibiane Roberts, Michael |
| author_facet | Zhang, Fan Kreuter, Daniel Esteve-Yagüe, Carlos Dittmer, Sören Fernandez-Marques, Javier Ip, Samantha Consortium, BloodCounts! de Wit, Norbert C. J. Wood, Angela Rudd, James HF Lane, Nicholas Gleadall, Nicholas S Schönlieb, Carola-Bibiane Roberts, Michael |
| contents | Federated learning (FL) promises to enable collaborative machine learning across healthcare sites whilst preserving data privacy. Practical deployment remains limited by statistical heterogeneity arising from differences in patient demographics, treatments, and outcomes, and infrastructure constraints. We introduce FedMAP, a personalised FL (PFL) framework that addresses heterogeneity through local Maximum a Posteriori (MAP) estimation with Input Convex Neural Network priors. These priors represent global knowledge gathered from other sites that guides the model while adapting to local data, and we provide a formal proof of convergence. Unlike many PFL methods that rely on fixed regularisation, FedMAP's prior adaptively learns patterns that capture complex inter-site relationships. We demonstrate improved performance compared to local training, FedAvg, and several PFL methods across three large-scale clinical datasets: 10-year cardiovascular risk prediction (CPRD, 387 general practitioner practices, 258,688 patients), iron deficiency detection (INTERVAL, 4 donor centres, 31,949 blood donors), and mortality prediction (eICU, 150 hospitals, 44,842 patients). FedMAP incorporates a three-tier design that enables participation across healthcare sites with varying infrastructure and technical capabilities, from full federated training to inference-only deployment. Geographical analysis reveals substantial equity improvements, with underperforming regions achieving up to 14.3% performance gains. This framework provides the first practical pathway for large-scale healthcare FL deployment, which ensures clinical sites at all scales can benefit, equity is enhanced, and privacy is retained. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19000 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systems Zhang, Fan Kreuter, Daniel Esteve-Yagüe, Carlos Dittmer, Sören Fernandez-Marques, Javier Ip, Samantha Consortium, BloodCounts! de Wit, Norbert C. J. Wood, Angela Rudd, James HF Lane, Nicholas Gleadall, Nicholas S Schönlieb, Carola-Bibiane Roberts, Michael Machine Learning Federated learning (FL) promises to enable collaborative machine learning across healthcare sites whilst preserving data privacy. Practical deployment remains limited by statistical heterogeneity arising from differences in patient demographics, treatments, and outcomes, and infrastructure constraints. We introduce FedMAP, a personalised FL (PFL) framework that addresses heterogeneity through local Maximum a Posteriori (MAP) estimation with Input Convex Neural Network priors. These priors represent global knowledge gathered from other sites that guides the model while adapting to local data, and we provide a formal proof of convergence. Unlike many PFL methods that rely on fixed regularisation, FedMAP's prior adaptively learns patterns that capture complex inter-site relationships. We demonstrate improved performance compared to local training, FedAvg, and several PFL methods across three large-scale clinical datasets: 10-year cardiovascular risk prediction (CPRD, 387 general practitioner practices, 258,688 patients), iron deficiency detection (INTERVAL, 4 donor centres, 31,949 blood donors), and mortality prediction (eICU, 150 hospitals, 44,842 patients). FedMAP incorporates a three-tier design that enables participation across healthcare sites with varying infrastructure and technical capabilities, from full federated training to inference-only deployment. Geographical analysis reveals substantial equity improvements, with underperforming regions achieving up to 14.3% performance gains. This framework provides the first practical pathway for large-scale healthcare FL deployment, which ensures clinical sites at all scales can benefit, equity is enhanced, and privacy is retained. |
| title | FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.19000 |