PPFL: A Personalized Federated Learning Framework for Heterogeneous Population
Published Online:25 Aug 2025https://doi.org/10.1287/ijoc.2023.0376
References
- (2022) Precision medicine in stroke: Towards personalized outcome predictions using artificial intelligence. Brain 145(2):457–475.Crossref, Google Scholar
- (2022) Towards understanding the mixture-of-experts layer in deep learning. Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho K, Oh A, eds. NIPS’22: Proc. 36th Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 23049–23062.Google Scholar
- (2021) Exploiting shared representations for personalized federated learning. Marina M, Tong Z, eds. Internat. Conf. Machine Learn. (PMLR, Cambridge, MA), 10126–10146.Google Scholar
- (2015) Stochastic block mirror descent methods for nonsmooth and stochastic optimization. SIAM J. Optim. 25(2):856–881.Crossref, Google Scholar
- (2021) Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine 27(10):1735–1743.Crossref, Google Scholar
- (2025) PPFL: A personalized federated learning framework for heterogeneous population. https://doi.org/10.1287/ijoc.2023.0376, https://github.com/INFORMSJoC/2023.0376.Google Scholar
- (2020) Personalized federated learning with Moreau envelopes. Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, eds. NIPS’20: Proc. 34th Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 21394–21405.Google Scholar
- (2018) A mixture of personalized experts for human affect estimation. Perner P, ed. Machine Learn. Data Mining Pattern Recognition. MLDM 2018, Lecture Notes in Computer Science, vol. 10935 (Springer, Cham, Switzerland), 316–330.Google Scholar
- (2020) A learning framework for personalized random utility maximization (rum) modeling of user behavior. IEEE Trans. Automation Sci. Engrg. 19(1):510–521.Crossref, Google Scholar
- (2020) An efficient framework for clustered federated learning. Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, eds. NIPS’20: Proc. 34th Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 19586–19597.Google Scholar
- (2023) Personalized federated learning: A unified framework and universal optimization techniques. Trans. Machine Learn. Res.Google Scholar
- (2022) Factorized-FL: Personalized federated learning with parameter factorization & similarity matching. Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho K, Oh A, eds. NIPS’22: Proc. 36th Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 35684–35695.Google Scholar
- (2023) Federated learning from small datasets. Kamalika C, Stefanie J, Le S, Csaba S, Gang N, Sivan S, eds. Internat. Conf. Learn. Representations (PMLR, Cambridge, MA), 14800–14819.Google Scholar
- (2022) Integrating textual information into models of choice and scaled response data. Marketing Sci. 41(4):815–830.Link, Google Scholar
- (2025) FCOM: A federated collaborative online monitoring framework via representation learning. Proc. AAAI Conf. Artificial Intelligence (AAAI Press, Menlo Park, CA), 17957–17965.Google Scholar
- (2021) Ditto: Fair and robust federated learning through personalization. Marina M, Tong Z, eds. Internat. Conf. Machine Learn. (PMLR, Cambridge, MA), 6434–6444.Google Scholar
- (2020) Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37(3):50–60.Crossref, Google Scholar
- (2020) Think locally, act globally: Federated learning with local and global representations. Preprint, submitted January 6, https://arxiv.org/abs/2001.01523.Google Scholar
- (2021) PFA: Privacy-preserving federated adaptation for effective model personalization. WWW’21: Proc. Web Conf. (Association for Computing Machinery, New York), 923–934.Google Scholar
- (2018) Selective sensing of a heterogeneous population of units with dynamic health conditions. IISE Trans. 50(12):1076–1088.Crossref, Google Scholar
- (2017) A collaborative learning framework for estimating many individualized regression models in a heterogeneous population. IEEE Trans. Reliability 67(1):328–341.Crossref, Google Scholar
- (2021) Federated multi-task learning under a mixture of distributions. Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Wortman Vaughan J, eds. NIPS’21: Proc. 35th Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 15434–15447.Google Scholar
- (2017) Communication-efficient learning of deep networks from decentralized data. Aarti A, Jerry Z, eds. Proc. 20th Internat. Conf. Artificial Intelligence Statist. (PMLR, Cambridge, MA), 1273–1282.Google Scholar
- (2019) Definitions, methods, and applications in interpretable machine learning. Proc. Natl. Acad. Sci. USA 116(44):22071–22080.Crossref, Google Scholar
- (2019) On the positive semi-definite property of similarity matrices. Theoret. Comput. Sci. 755:13–28.Crossref, Google Scholar
- (2022) Federated learning with partial model personalization. Kamalika C, Stefanie J, Le S, Csaba S, Gang N, Sivan S, eds. Internat. Conf. Machine Learn. (PMLR, Cambridge, MA), 17773–17807.Google Scholar
- (2020) Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints. IEEE Trans. Neural Networks Learn. Systems 32(8):3710–3722.Crossref, Google Scholar
- (2017) Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. Internat. Conf. Learn. Representations (Curran Associates, Inc., Red Hook, NY), 875–888.Google Scholar
- (2023) Optimal price targeting. Marketing Sci. 42(3):476–499.Link, Google Scholar
- (2017) Federated multi-task learning. von Luxburg U, Guyon I, Bengio S, Wallach H, Fergus R, eds. NIPS’17: Proc. 31st Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 4427–4437.Google Scholar
- (2022) Towards personalized federated learning. IEEE Trans. Neural Networks Learn. Systems 34(12):9587–9603.Crossref, Google Scholar
- (2019) Distributed majorization-minimization for Laplacian regularized problems. IEEE/CAA J. Automatica Sinica 6(1):45–52.Crossref, Google Scholar
- (1996) A linear mixed-effects model with heterogeneity in the random-effects population. J. Amer. Statist. Assoc. 91(433):217–221.Crossref, Google Scholar
- (2021) Federated block coordinate descent scheme for learning global and personalized models. Proc. AAAI Conf. Artificial Intelligence 35(12):10355–10362.Crossref, Google Scholar
- (2021) Privacy-preserving cost-sensitive learning. IEEE Trans. Neural Networks Learn. Systems 32(5):2105–2116.Crossref, Google Scholar
- (2024) Federated data analytics: A study on linear models. IISE Trans. 56(1):16–28.Crossref, Google Scholar
- (2023) FedDAR: Federated domain-aware representation learning. Internat. Conf. Learn. Representations (Curran Associates, Inc., Red Hook, NY), 11210–11241.Google Scholar
- (2023) Federated learning via inexact ADMM. IEEE Trans. Pattern Anal. Machine Intelligence 45(8):9699–9708.Crossref, Google Scholar
- (2020) Laplacian regularized few-shot learning. Internat. Conf. Machine Learn. (PMLR, Cambridge, MA), 14521–14532.Google Scholar

