# FedCode: Communication-Efficient Federated Learning via Transferring Codebooks

arXiv: https://arxiv.org/abs/2311.09270 · HTML: https://mlsanity.com/paper/2311.09270/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 48 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [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)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Pruning neural networks without any data by iteratively conserving synaptic flow](https://mlsanity.com/paper/2006.05467/) (2020)
- [Sparsified SGD with Memory](https://mlsanity.com/paper/1809.07599/) (2018)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Quantized Convolutional Neural Networks for Mobile Devices](https://mlsanity.com/paper/1512.06473/) (2015)
- [One-Shot Federated Learning](https://mlsanity.com/paper/1902.11175/) (2019)
- [Distilled One-Shot Federated Learning](https://mlsanity.com/paper/2009.07999/) (2020)
- [Fast-Convergent Federated Learning with Adaptive Weighting](https://mlsanity.com/paper/2012.00661/) (2020)
- [Fast-Convergent Federated Learning](https://mlsanity.com/paper/2007.13137/) (2020)
- [EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning](https://mlsanity.com/paper/2108.08842/) (2021)
- [DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning](https://mlsanity.com/paper/2111.00465/) (2021)
- [A Distributed Synchronous SGD Algorithm with Global Top-k Sparsification for Low Bandwidth Networks](https://mlsanity.com/paper/1901.04359/) (2019)
- [Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions](https://mlsanity.com/paper/1806.09228/) (2018)
- [FEDZIP: A Compression Framework for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2102.01593/) (2021)
- [ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training](https://mlsanity.com/paper/2110.05323/) (2021)
- [ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning](https://mlsanity.com/paper/1905.00136/) (2019)
- [Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates](https://mlsanity.com/paper/2105.04153/) (2021)
- [Federated Progressive Sparsification (Purge, Merge, Tune)+](https://mlsanity.com/paper/2204.12430/) (2022)
- [Online Model Compression for Federated Learning with Large Models](https://mlsanity.com/paper/2205.03494/) (2022)

## Built on by (most cited first, 3 of 3)

- [EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters](https://mlsanity.com/paper/2406.09152/) (2024)
- [Federated Fine-Tuning of Foundation Models via Probabilistic Masking](https://mlsanity.com/paper/2311.17299/) (2023)
- [EFU: Enforcing Federated Unlearning via Functional Encryption](https://mlsanity.com/paper/2508.07873/) (2025)

## Abstract (arXiv)

Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized local data. While FL offers appealing properties for clients' data privacy, it imposes high communication burdens for exchanging model weights between a server and the clients. Existing approaches rely on model compression techniques, such as pruning and weight clustering to tackle this. However, transmitting the entire set of weight updates at each federated round, even in a compressed format, limits the potential for a substantial reduction in communication volume. We propose FedCode where clients transmit only codebooks, i.e., the cluster centers of updated model weight values. To ensure a smooth learning curve and proper calibration of clusters between the server and the clients, FedCode periodically transfers model weights after multiple rounds of solely communicating codebooks. This results in a significant reduction in communication volume between clients and the server in both directions, without imposing significant computational overhead on the clients or leading to major performance degradation of the models. We evaluate the effectiveness of FedCode using various publicly available datasets with ResNet-20 and MobileNet backbone model architectures. Our evaluations demonstrate a 12.2-fold data transmission reduction on average while maintaining a comparable model performance with an average accuracy loss of 1.3% compared to FedAvg. Further validation of FedCode performance under non-IID data distributions showcased an average accuracy loss of 2.0% compared to FedAvg while achieving approximately a 12.7-fold data transmission reduction.