# Bayesian Coreset Optimization for Personalized Federated Learning

arXiv: https://arxiv.org/abs/2511.01800 · HTML: https://mlsanity.com/paper/2511.01800/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 51 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 30 of 30)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [Selection via Proxy: Efficient Data Selection for Deep Learning](https://mlsanity.com/paper/1906.11829/) (2019)
- [Not All Samples Are Created Equal: Deep Learning with Importance Sampling](https://mlsanity.com/paper/1803.00942/) (2018)
- [Personalized Federated Learning: A Meta-Learning Approach](https://mlsanity.com/paper/2002.07948/) (2020)
- [GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training](https://mlsanity.com/paper/2103.00123/) (2021)
- [The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2102.06387/) (2021)
- [Data Valuation using Reinforcement Learning](https://mlsanity.com/paper/1909.11671/) (2019)
- [Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent](https://mlsanity.com/paper/1802.01737/) (2018)
- [Personalized Federated Learning via Variational Bayesian Inference](https://mlsanity.com/paper/2206.07977/) (2022)
- [Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision](https://mlsanity.com/paper/1901.01151/) (2019)
- [Personalized Federated Learning through Local Memorization](https://mlsanity.com/paper/2111.09360/) (2021)
- [Posterior Concentration for Sparse Deep Learning](https://mlsanity.com/paper/1803.09138/) (2018)
- [Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee](https://mlsanity.com/paper/2011.07439/) (2020)
- [PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion](https://mlsanity.com/paper/2110.10926/) (2021)
- [Sparse Variational Inference: Bayesian Coresets from Scratch](https://mlsanity.com/paper/1906.03329/) (2019)
- [Personalized Federated Learning with Adaptive Batchnorm for Healthcare](https://mlsanity.com/paper/2112.00734/) (2021)
- [Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation](https://mlsanity.com/paper/2208.09375/) (2022)
- [On Statistical Optimality of Variational Bayes](https://mlsanity.com/paper/1712.08983/) (2017)
- [Dual Personalization on Federated Recommendation](https://mlsanity.com/paper/2301.08143/) (2023)
- [Semi-Supervised Data Programming with Subset Selection](https://mlsanity.com/paper/2008.09887/) (2020)
- [Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering](https://mlsanity.com/paper/2210.14664/) (2022)
- [Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks](https://mlsanity.com/paper/1805.11191/) (2018)
- [Submodlib: A Submodular Optimization Library](https://mlsanity.com/paper/2202.10680/) (2022)

## Built on by (most cited first, 1 of 1)

- [Bandit Guided Submodular Curriculum for Adaptive Subset Selection](https://mlsanity.com/paper/2511.22944/) (2025)

## Abstract (arXiv)

In a distributed machine learning setting like Federated Learning where there are multiple clients involved which update their individual weights to a single central server, often training on the entire individual client's dataset for each client becomes cumbersome. To address this issue we propose methodprop: a personalized coreset weighted federated learning setup where the training updates for each individual clients are forwarded to the central server based on only individual client coreset based representative data points instead of the entire client data. Through theoretical analysis we present how the average generalization error is minimax optimal up to logarithm bounds (upper bounded by O(nk^-frac2 β2 β+boldsymbolLambda log ^2 δprime(nk))) and lower bounds of O(nk^-frac2 β2 β+boldsymbolLambda), and how the overall generalization error on the data likelihood differs from a vanilla Federated Learning setup as a closed form function boldsymbolIm(boldsymbolw, nk) of the coreset weights boldsymbolw and coreset sample size nk. Our experiments on different benchmark datasets based on a variety of recent personalized federated learning architectures show significant gains as compared to random sampling on the training data followed by federated learning, thereby indicating how intelligently selecting such training samples can help in performance. Additionally, through experiments on medical datasets our proposed method showcases some gains as compared to other submodular optimization based approaches used for subset selection on client's data.