# SecureFLCompression

**Repository Path**: facebookresearch/SecureFLCompression

## Basic Information

- **Project Name**: SecureFLCompression
- **Description**: Compression primitives for uplink compression in Federated Learning that are compatible with Secure Aggregation.
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-07-31
- **Last Updated**: 2023-07-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Secure Federated Learning Compression

This repository contains the code to reproduce the results of the paper [Reconciling Security and Communication Efficiency in Federated Learning](https://arxiv.org/abs/2207.12779). We implement various compression methods (scalar quantization, random pruning, product quantization) for compressing (uplink) model updates sent from the clients to the server in FLSim. The implementation modifies the underling compression methods to be compatible with the Secure Aggregation protocol, which ensures that the server only sees the aggregated model updates across all clients participating in the current round. The results presented in the code were run on FLSim version 0.1.0

<p align="center">
  <img src="assets/summary.png">
</p>

## Installation

The dependencies can be installed with: `pip install -r requirements.txt`.

## Results

To reproduce our results, on say the CelebA dataset, run `cd examples/`, follow the instructions in `celeba_example.py` to download the dataset and partition it by users and finally run:
```
python celeba_example.py --config-file configs/target_config_file.json
```
where `target_config_file.json` can be set to:
- `celeba_config.json` for uncompressed baseline
- `celeba_sq_config.json` for scalar quantization with Secure Aggregation
- `celeba_pruning_config.json` for Random Pruning with Secure Aggregation
- `celeba_pq_config.json` for PQ with SecureIndexing

Feel free to vary the arguments of the "channel" field to test all the possible compression ratios!


## License
This code is released under Apache 2.0, as found in the [LICENSE](https://github.com/facebookresearch/SecureFLCompression/blob/main/LICENSE) file.

## Bibliography

Please consider citing [1] if you found the resources in this repository and/or the paper useful.

[1] Prasad, Karthik and Ghosh, Sayan and Cormode, Graham and Mironov, Ilya and Yousefpour, Ashkan and Stock, Pierre. [Reconciling Security and Communication Efficiency in Federated Learning](https://arxiv.org/abs/2207.12779).

```
@misc{prasad2022reconciling,
  title = {Reconciling Security and Communication Efficiency in Federated Learning},
  author = {Prasad, Karthik and Ghosh, Sayan and Cormode, Graham and Mironov, Ilya and Yousefpour, Ashkan and Stock, Pierre},
  year = {2022}
}
```