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JinsooKim-KR committed Sep 28, 2023
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Expand Up @@ -33,17 +33,30 @@ dataset: [FEMNIST, SHAKESPEARE] # list of datasets you include in your baseline
* A two-layer CNN network as used in the FedMeta paper (see `models/CNN_Network`). This is the model used by default.
* A StackedLSTM model used in the FedMeta paper for Shakespeare (see `models/StackedLSTM`).

**You can see more detail model at Apendix.A in paper**
**You can see more detail at Apendix.A in paper**

****Dataset:**** : This baseline includes the FEMNIST dataset and SHAKESPEARE. Now you should include a breakdown of the details about each of them. Please include information about: how the dataset is partitioned (e.g. LDA with alpha 0.1 as default and all clients have the same number of training examples; or each client gets assigned a different number of samples following a power-law distribution with each client only instances of 2 classes)? if your dataset is naturally partitioned just state “naturally partitioned”; how many partitions there are (i.e. how many clients)? Please include this an all information relevant about the dataset and its partitioning into a table.
****Dataset:**** : This baseline includes the FEMNIST dataset and SHAKESPEARE. For data partitioning and sampling per client, we use the Leaf GitHub([LEAF: A Benchmark for Federated Settings](https://github.com/TalwalkarLab/leaf)). The data and client specifications used in this experiment are listed in the table below (Table 1 in the paper).

| Dataset | #Clients | #Samples | #Classes | partition settings |
|:-----------:|:--------:| :---: |:--------:|:---------------------------------------------:|
| FEMNIST | 1,068 | 235,683 | 62 | Support set : 0.2, Query set : 0.8 |
| SHAKESPEARE | 110 | 625,127 | 80 | Support set : 0.2, Query set : 0.8 |
| Dataset | #Clients | #Samples | #Classes | #Partition Clients | #Partition Dataset |
|:-----------:|:--------:| :---: |:--------:|:------------------------------------------------------------:|-----------------------------------|
| FEMNIST | 1,068 | 235,683 | 62 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Support set : 0.8, Query set : 0.2 |
| SHAKESPEARE | 110 | 625,127 | 80 | Train Clients : 0.8, Valid Clients : 0.1, Test Clients : 0.1 | Support set : 0.8, Query set : 0.2|

**The original specifications of the Leaf dataset can be found in the Leaf paper(_"LEAF: A Benchmark for Federated Settings"_).**

****Training Hyperparameters:**** :warning: *_Include a table with all the main hyperparameters in your baseline. Please show them with their default value._*

| Algorithm | Dataset | clients per round | number of rounds | batch size | optimizer | Learning Rate(α, β) | client resources |
|:-----------------:|:--------------:|:-------------------:|:------------------:|:-----------:|:---------:|:-------------------:|--------------------------------------|
| FedAvg | FEMNST | 4(Defalut), 40, 50 | 2000 | 10 | Adam | 0.0001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } |
| FedAVg | SHAKESPEARE | 4(Defalut), 40, 50 | 400 | 10 | Adam | 0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } |
| FedAvg(Meta) | FEMNST | 4(Defalut), 40, 50 | 2000 | 10 | Adam | 0.0001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } |
| FedAvg(Meta) | SHAKESPEARE | 4(Defalut), 40, 50 | 400 | 10 | Adam | 0.001 | {'num_cpus': 4.0, 'num_gpus': 0.25 } |
| FedMeta(MAML) | FEMNST | 4(Defalut), 40, 50 | 2000 | 10 | Adam | (0.001,0.0001) | {'num_cpus': 4.0, 'num_gpus': 0.25 } |
| FedMeta(MAML) | SHAKESPEARE | 4(Defalut), 40, 50 | 400 | 10 | Adam | (0.1,0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } |
| FedMeta(Meta-SGD | FEMNST | 4(Defalut), 40, 50 | 2000 | 10 | Adam | (0.001,0.0001) | {'num_cpus': 4.0, 'num_gpus': 0.25 } |
| FedMeta(Meta-SGD | SHAKESPEARE | 4(Defalut), 40, 50 | 400 | 10 | Adam | (0.1,0.01) | {'num_cpus': 4.0, 'num_gpus': 1.0 } |


## Environment Setup

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