FedMann: A Manually Coordinated Federated Learning Framework Applied to Multiple Myeloma—A Feasibility Study
6 affiliations
- 1VA Boston Healthcare System, Boston, MA 02301, USA
- 2Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA
- 3Section of Hematology and Medical Oncology, Boston Medical Center, Boston, MA 02118, USA
- 4Accenture Federal Services, Arlington, VA 22203, USA
- 5Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- 6The Jerome Lipper Multiple Myeloma Center, Dana-Farber Cancer Institute, Boston, MA 02215, USA
Abstract
Background/Objectives: Federated learning (FL) enables multi-institutional model development in oncology without sharing patient-level data, but automated FL infrastructures typically require dedicated servers, firewall modifications, and substantial IT support to deploy. These requirements may be prohibitive for smaller institutions or consortia with limited IT resources or stringent information security requirements. We developed FedMann, a manually coordinated FL framework, and demonstrated its feasibility by applying it to prediction of suboptimal multiple myeloma (MM) treatment across two clinical sites with substantially different patient populations. Methods: This retrospective cohort study used electronic health record data from patients newly diagnosed with MM at the Veterans Affairs (VA) Healthcare System or Boston Medical Center (BMC) between 2012 and 2022. Demographic, clinical, and geographic predictors were used to predict non-receipt of guideline-concordant primary therapy, defined as not receiving a frontline triplet or quadruplet regimen or autologous stem cell transplant when eligible. FedMann was implemented as a four-stage cycle of local training, parameter sharing through an access-controlled repository, sample-size-weighted parameter averaging at a coordinating site, and federated model redistribution. Models were evaluated on site-specific held-out test sets using area under the receiver operating characteristic curve (AUC) with bootstrap-resampled 95% confidence intervals. Results: VA and BMC contributed 4887 and 261 cases, respectively, with substantial demographic differences across all measured characteristics including sex distribution (96.3% male at VA vs. 58.6% at BMC; p < 0.001). The BMC-only model achieved an AUC of 0.70 (95% CI, 0.56–0.81) on the BMC test set. The federated model achieved 0.68 (95% CI, 0.55–0.80) on the BMC test set and 0.70 (95% CI, 0.67–0.72) on the VA test set. Conclusions: A manually coordinated FL framework can support multi-institutional model fitting in oncology without exchanging patient-level data and without requiring dedicated FL infrastructure. In a two-site MM application, performance was comparable between the federated model and each site’s own local model, and the VA only model showed no apparent loss of discrimination when externally validated at BMC, with no statistically significant differences in AUC on the BMC test set. Although absolute AUCs were modest, reflecting limited cohort size and a small predictor set, this application demonstrates the feasibility of the framework as an alternative when automated FL deployment is impractical.
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BibTeX
@article{sommers2026fedmann,
title = {{FedMann: A Manually Coordinated Federated Learning Framework Applied to Multiple Myeloma—A Feasibility Study}},
author = {Stuart W. Sommers and Chunlei Zheng and June K. Corrigan and John M. Culnan and Geethika Bodanapu and Grace M. Ferri and Maya Abdallah and Britney Bell and Meghana Yellanki and Danne C. Elbers and Nhan V. Do and Mary T. Brophy and Nikhil C. Munshi and Camille V. Edwards and Nathanael R. Fillmore},
journal = {Journal of Clinical Medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15218418},
url = {https://www.mdpi.com/2077-0383/15/21/8418},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - FedMann: A Manually Coordinated Federated Learning Framework Applied to Multiple Myeloma—A Feasibility Study
AU - Stuart W. Sommers
AU - Chunlei Zheng
AU - June K. Corrigan
AU - John M. Culnan
AU - Geethika Bodanapu
AU - Grace M. Ferri
AU - Maya Abdallah
AU - Britney Bell
AU - Meghana Yellanki
AU - Danne C. Elbers
AU - Nhan V. Do
AU - Mary T. Brophy
AU - Nikhil C. Munshi
AU - Camille V. Edwards
AU - Nathanael R. Fillmore
PY - 2026
DA - 2026/10/29
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15218418
UR - https://www.mdpi.com/2077-0383/15/21/8418
AB - Background/Objectives: Federated learning (FL) enables multi-institutional model development in oncology without sharing patient-level data, but automated FL infrastructures typically require dedicated servers, firewall modifications, and substantial IT support to deploy. These requirements may be prohibitive for smaller institutions or consortia with limited IT resources or stringent information security requirements. We developed FedMann, a manually coordinated FL framework, and demonstrated its feasibility by applying it to prediction of suboptimal multiple myeloma (MM) treatment across two clinical sites with substantially different patient populations. Methods: This retrospective cohort study used electronic health record data from patients newly diagnosed with MM at the Veterans Affairs (VA) Healthcare System or Boston Medical Center (BMC) between 2012 and 2022. Demographic, clinical, and geographic predictors were used to predict non-receipt of guideline-concordant primary therapy, defined as not receiving a frontline triplet or quadruplet regimen or autologous stem cell transplant when eligible. FedMann was implemented as a four-stage cycle of local training, parameter sharing through an access-controlled repository, sample-size-weighted parameter averaging at a coordinating site, and federated model redistribution. Models were evaluated on site-specific held-out test sets using area under the receiver operating characteristic curve (AUC) with bootstrap-resampled 95% confidence intervals. Results: VA and BMC contributed 4887 and 261 cases, respectively, with substantial demographic differences across all measured characteristics including sex distribution (96.3% male at VA vs. 58.6% at BMC; p < 0.001). The BMC-only model achieved an AUC of 0.70 (95% CI, 0.56–0.81) on the BMC test set. The federated model achieved 0.68 (95% CI, 0.55–0.80) on the BMC test set and 0.70 (95% CI, 0.67–0.72) on the VA test set. Conclusions: A manually coordinated FL framework can support multi-institutional model fitting in oncology without exchanging patient-level data and without requiring dedicated FL infrastructure. In a two-site MM application, performance was comparable between the federated model and each site’s own local model, and the VA only model showed no apparent loss of discrimination when externally validated at BMC, with no statistically significant differences in AUC on the BMC test set. Although absolute AUCs were modest, reflecting limited cohort size and a small predictor set, this application demonstrates the feasibility of the framework as an alternative when automated FL deployment is impractical.
SN - 2077-0383
ER -