# TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients

arXiv: https://arxiv.org/abs/2401.12012 · HTML: https://mlsanity.com/paper/2401.12012/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Federated Learning with Only Positive Labels](https://mlsanity.com/paper/2004.10342/) (2020)
- [Learning Spread-out Local Feature Descriptors](https://mlsanity.com/paper/1708.06320/) (2017)
- [Local Adaptivity in Federated Learning: Convergence and Consistency](https://mlsanity.com/paper/2106.02305/) (2021)
- [Toward Communication Efficient Adaptive Gradient Method](https://mlsanity.com/paper/2109.05109/) (2021)

## Built on by (most cited first, 7 of 7)

- [CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning](https://mlsanity.com/paper/2511.18611/) (2025)
- [pSVM: Soft-margin SVMs with p-norm Hinge Loss](https://mlsanity.com/paper/2408.09908/) (2024)
- [fluke: Federated Learning Utility frameworK for Experimentation and research](https://mlsanity.com/paper/2412.15728/) (2024)
- [Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning](https://mlsanity.com/paper/2505.09959/) (2025)
- [CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classification](https://mlsanity.com/paper/2510.17584/) (2025)
- [Benchmark Evaluation of Federated Learning on Multi-organ Images](https://mlsanity.com/paper/2607.08219/) (2026)
- [Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities](https://mlsanity.com/paper/2609.28722/) (2026)

## Abstract (arXiv)

Federated learning is a distributed collaborative machine learning paradigm that has gained strong momentum in recent years. In federated learning, a central server periodically coordinates models with clients and aggregates the models trained locally by clients without necessitating access to local data. Despite its potential, the implementation of federated learning continues to encounter several challenges, predominantly the slow convergence that is largely due to data heterogeneity. The slow convergence becomes particularly problematic in cross-device federated learning scenarios where clients may be strongly limited by computing power and storage space, and hence counteracting methods that induce additional computation or memory cost on the client side such as auxiliary objective terms and larger training iterations can be impractical. In this paper, we propose a novel federated aggregation strategy, TurboSVM-FL, that poses no additional computation burden on the client side and can significantly accelerate convergence for federated classification task, especially when clients are "lazy" and train their models solely for few epochs for next global aggregation. TurboSVM-FL extensively utilizes support vector machine to conduct selective aggregation and max-margin spread-out regularization on class embeddings. We evaluate TurboSVM-FL on multiple datasets including FEMNIST, CelebA, and Shakespeare using user-independent validation with non-iid data distribution. Our results show that TurboSVM-FL can significantly outperform existing popular algorithms on convergence rate and reduce communication rounds while delivering better test metrics including accuracy, F1 score, and MCC.