# FedVQCS: Federated Learning via Vector Quantized Compressed Sensing

arXiv: https://arxiv.org/abs/2204.07692 · HTML: https://mlsanity.com/paper/2204.07692/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2022.

## Built on by (most cited first, 9 of 9)

- [Communication-Efficient Split Learning via Adaptive Feature-Wise Compression](https://mlsanity.com/paper/2307.10805/) (2023)
- [Tighter Regret Analysis and Optimization of Online Federated Learning](https://mlsanity.com/paper/2205.06491/) (2022)
- [Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks](https://mlsanity.com/paper/2501.11267/) (2025)
- [Communication Efficient Split Learning of ViTs with Attention-based Double Compression](https://mlsanity.com/paper/2509.15058/) (2025)
- [Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey](https://mlsanity.com/paper/2509.21389/) (2025)
- [FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization](https://mlsanity.com/paper/2404.13575/) (2024)
- [Random Orthogonalization for Federated Learning in Massive MIMO Systems](https://mlsanity.com/paper/2210.09881/) (2022)
- [Quantization in Federated Learning: Methods, Challenges and Future Directions](https://mlsanity.com/paper/2606.26822/) (2026)
- [Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning](https://mlsanity.com/paper/2607.13119/) (2026)

## Abstract (arXiv)

In this paper, a new communication-efficient federated learning (FL) framework is proposed, inspired by vector quantized compressed sensing. The basic strategy of the proposed framework is to compress the local model update at each device by applying dimensionality reduction followed by vector quantization. Subsequently, the global model update is reconstructed at a parameter server by applying a sparse signal recovery algorithm to the aggregation of the compressed local model updates. By harnessing the benefits of both dimensionality reduction and vector quantization, the proposed framework effectively reduces the communication overhead of local update transmissions. Both the design of the vector quantizer and the key parameters for the compression are optimized so as to minimize the reconstruction error of the global model update under the constraint of wireless link capacity. By considering the reconstruction error, the convergence rate of the proposed framework is also analyzed for a non-convex loss function. Simulation results on the MNIST and FEMNIST datasets demonstrate that the proposed framework provides more than a 2.4% increase in classification accuracy compared to state-of-the-art FL frameworks when the communication overhead of the local model update transmission is 0.1 bit per local model entry.