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Zhifeng JIANG committed Mar 6, 2023
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# Pisces

This repository contains the evvaluation artifacts of our SoCC'22 paper
[Efficient Federated Learning via Guided Asynchronous Training](http://home.cse.ust.hk/~zjiangaj/files/pisces-socc22.pdf).
This repository contains the evaluation artifacts of our SoCC'22 paper
[Efficient Federated Learning via Guided Asynchronous Training](https://dl.acm.org/doi/10.1145/3542929.3563463).
It is implemented by extending an early version of [Plato](https://github.com/TL-System/plato), a new scalable federated learning research framework.

## 1. Overview
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## 3. Running Experiments


### 1. Configuring Tasks
### 3.1 Configuring Tasks

In the folder `[project folder]/experiments/exp_config_examples`,
you can find the configuration files for running the experiments
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by customizing the configuration files.
The above-mentioned examples can serve as templates for you.

### 2. Running a Particular Task
### 3.2 Running a Particular Task

Once you have prepared a legitimate configuration file for a task,
you can put it in your created experiment folder and then use
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```bibtex
@inproceedings{pisces-socc22,
author={Jiang, Zhifeng and Wang, Wei and Li, Baochun and Li, Bo},
title={Pisces: Efficient Federated Learning via Guided Asynchronous Training},
author={Zhifeng Jiang and Wei Wang and Baochun Li and Bo Li},
booktitle={ACM Symposium on Cloud Computing (SoCC)},
year={2022}
year={2022},
isbn={9781450394147},
publisher={Association for Computing Machinery},
booktitle={Proceedings of the 13th Symposium on Cloud Computing},
url={https://doi.org/10.1145/3542929.3563463},
doi={10.1145/3542929.3563463},
pages={370–385},
}
```

## Contact
Zhifeng Jiang ([email protected]).
Zhifeng Jiang ([email protected]).

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