Awesome

Federated Few-shot Learning for Mobile NLP (FeS)

FeS (old name: NFS) is an training-inference orchestration framework for enable private mobile NLP training with few labels.

FeS is built atop Pattern-Exploiting Training (PET) (commit id: 21d32d), the document file and instruction could be found in README_pet.md

Step-by-step installation

After git clone-ing this repository, please run the following command to install our dependencies.

# create directly via conda (recommended)
conda env create -f environment.yml
# or you can create the environment manually
conda create -n nfs python=3.7
conda activate nfs
pip install -r requirements.txt

System requirement

Our system is implemented on:

Linux Phoenix22 5.4.0-122-generic #138~18.04.1-Ubuntu SMP Fri Jun 24 14:14:03 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux

Download data

# remebmer to modify the path in download.sh
bash script_shell/download.sh

Main experiemnts

# RoBERTa-large
conda activate nfs
python sweep_aug.py

Above shells will run all datasets and models for three times (different seeds) automatically.The log files would be placed into log/ablation.

Reproduce main results in the paper

We process the result log via figs/script to get the final pictures in the manuscript.

Our experiment results could be downloaded from Google drive.

Customization

1. Manully run

First, you should turn off line 118 to auto = False in sweep_aug.py. Now you have following options to customize your experiments:

--dataset DATASET     Available datasets: agnews, mnli, yahoo, yelp-full
--method METHOD       Available methods: fedclassifier, fedpet
--device DEVICE       CUDA_VISIABLE_DEVICE
--train_examples TRAIN_EXAMPLES
                    done: 40; todo: 10, 100, 1000
--test_examples TEST_EXAMPLES
                    8700 for mnli
--unlabeled_examples UNLABELED_EXAMPLES
                    392700 for mnli
--alpha ALPHA         Data label similarity of each client, the larger the
                    beta the similar data for each client
--beta BETA           Int similarity of each client, the larger the beta the
                    similar data for each client. 0 for off
--gamma GAMMA         The labeled data distribution density, the larger the
                    gamma the uniform the labeled data distributed
--client_num_in_total CLIENT_NUM_IN_TOTAL
                    How many clients owe labeled data?
--all_client_num_in_total ALL_CLIENT_NUM_IN_TOTAL
                    How many clients are sperated
--pattern_ids PATTERN_IDS
                    pattern_ids
--seed SEED           seed
--model MODEL         model
--model_name_or_path MODEL_NAME_OR_PATH
                    model_name_or_path
--data_point DATA_POINT
                    How many data is to be annotated. Now, the increase
                    ratio of augment data
--conver_point CONVER_POINT
                    After conver_point, clients with unlabeled data will
                    be involved.
--limit LIMIT         logits < limit will be dropped
--num_clients_infer NUM_CLIENTS_INFER
                    select how many clients to do soft label annotation
--infer_freq INFER_FREQ
                    the model trains for infer_freq rounds, and annotation
                    starts once
--vote_k VOTE_K       whether to use vote_k. vote_k is the percentage of
                    unlabeled data for inferring

2. More experiments

Access modeling.py line 58-68 to switch on/off key designs, e.g., filtering, bias-tuning, curriculum pacing, etc. Remembert to modify the running shell run_fed_aug.sh line 20-21 accordingly.