# FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation

arXiv: https://arxiv.org/abs/2411.15847 · HTML: https://mlsanity.com/paper/2411.15847/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 24 Nov 2024.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
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- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Data-Free Knowledge Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2105.10056/) (2021)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning](https://mlsanity.com/paper/2105.05883/) (2021)
- [Preservation of the Global Knowledge by Not-True Distillation in Federated Learning](https://mlsanity.com/paper/2106.03097/) (2021)
- [FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation](https://mlsanity.com/paper/2210.08285/) (2022)
- [Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination](https://mlsanity.com/paper/2305.10730/) (2023)
- [FAIRER: Fairness as Decision Rationale Alignment](https://mlsanity.com/paper/2306.15299/) (2023)
- [FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications](https://mlsanity.com/paper/2006.15632/) (2020)
- [GitFL: Adaptive Asynchronous Federated Learning using Version Control](https://mlsanity.com/paper/2211.12049/) (2022)
- [AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems](https://mlsanity.com/paper/2311.13166/) (2023)
- [Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning](https://mlsanity.com/paper/2311.13163/) (2023)
- [FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning](https://mlsanity.com/paper/2308.11333/) (2023)
- [FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios](https://mlsanity.com/paper/2407.12729/) (2024)
- [CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance](https://mlsanity.com/paper/2404.12850/) (2024)

## Abstract (arXiv)

Due to the advantages of privacy-preserving, Federated Learning (FL) is widely used in distributed machine learning systems. However, existing FL methods suffer from low-inference performance caused by data heterogeneity. Specifically, due to heterogeneous data, the optimization directions of different local models vary greatly, making it difficult for the traditional FL method to get a generalized global model that performs well on all clients. As one of the state-of-the-art FL methods, the mutation-based FL method attempts to adopt a stochastic mutation strategy to guide the model training towards a well-generalized area (i.e., flat area in the loss landscape). Specifically, mutation allows the model to shift within the solution space, providing an opportunity to escape areas with poor generalization (i.e., sharp area). However, the stochastic mutation strategy easily results in diverse optimal directions of mutated models, which limits the performance of the existing mutation-based FL method. To achieve higher performance, this paper proposes a novel mutation-based FL approach named FedQP, utilizing a quadratic programming strategy to regulate the mutation directions wisely. By biasing the model mutation towards the direction of gradient update rather than traditional random mutation, FedQP can effectively guide the model to optimize towards a well-generalized area (i.e., flat area). Experiments on multiple well-known datasets show that our quadratic programming-guided mutation strategy effectively improves the inference accuracy of the global model in various heterogeneous data scenarios.