# Towards Sparsified Federated Neuroimaging Models via Weight Pruning

arXiv: https://arxiv.org/abs/2208.11669 · HTML: https://mlsanity.com/paper/2208.11669/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 36 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 22 of 22)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://mlsanity.com/paper/1710.01878/) (2017)
- [Rethinking the Value of Network Pruning](https://mlsanity.com/paper/1810.05270/) (2018)
- [Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks](https://mlsanity.com/paper/2102.00554/) (2021)
- [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017)
- [Model Pruning Enables Efficient Federated Learning on Edge Devices](https://mlsanity.com/paper/1909.12326/) (2019)
- [Machine Learning with Membership Privacy using Adversarial Regularization](https://mlsanity.com/paper/1807.05852/) (2018)
- [Revisiting Membership Inference Under Realistic Assumptions](https://mlsanity.com/paper/2005.10881/) (2020)
- [Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better](https://mlsanity.com/paper/2112.09824/) (2021)
- [Towards Demystifying Membership Inference Attacks](https://mlsanity.com/paper/1807.09173/) (2018)
- [Scaling Language Model Size in Cross-Device Federated Learning](https://mlsanity.com/paper/2204.09715/) (2022)
- [Membership Inference Attacks on Deep Regression Models for Neuroimaging](https://mlsanity.com/paper/2105.02866/) (2021)
- [Efficient passive membership inference attack in federated learning](https://mlsanity.com/paper/2111.00430/) (2021)
- [Modelling and Quantifying Membership Information Leakage in Machine Learning](https://mlsanity.com/paper/2001.10648/) (2020)
- [Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings](https://mlsanity.com/paper/2102.02849/) (2021)
- [Improved Brain Age Estimation with Slice-based Set Networks](https://mlsanity.com/paper/2102.04438/) (2021)
- [Scaling Neuroscience Research using Federated Learning](https://mlsanity.com/paper/2102.08440/) (2021)
- [Federated Progressive Sparsification (Purge, Merge, Tune)+](https://mlsanity.com/paper/2204.12430/) (2022)
- [An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks](https://mlsanity.com/paper/2009.08097/) (2020)

## Built on by (most cited first, 4 of 4)

- [Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach](https://mlsanity.com/paper/2604.21197/) (2026)
- [Federated Learning for Large Models in Medical Imaging: A Comprehensive Review](https://mlsanity.com/paper/2508.20414/) (2025)
- [United We Defend: Collaborative Membership Inference Defenses in Federated Learning](https://mlsanity.com/paper/2601.06866/) (2026)
- [Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction](https://mlsanity.com/paper/2506.15626/) (2025)

## Abstract (arXiv)

Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model pruning techniques have been designed in centralized settings to reduce inference times. Combining centralized pruning techniques with federated training seems intuitive for reducing communication costs -- by pruning the model parameters right before the communication step. Moreover, such a progressive model pruning approach during training can also reduce training times/costs. To this end, we propose FedSparsify, which performs model pruning during federated training. In our experiments in centralized and federated settings on the brain age prediction task (estimating a person's age from their brain MRI), we demonstrate that models can be pruned up to 95% sparsity without affecting performance even in challenging federated learning environments with highly heterogeneous data distributions. One surprising benefit of model pruning is improved model privacy. We demonstrate that models with high sparsity are less susceptible to membership inference attacks, a type of privacy attack.