Federated Submodel Averaging
An implementation of federated submodel averaging (FedSubAvg).
Data Process
Amazon-Electronic Dataset
data/: rating_only.csv is the raw user ratings for all items (rating=5 is considered as click, rating<5 is considered as non-click), preprocess.py process the data into the requierd format (user, item, click history, click, time), and data_partition.py split the data into a training set and a test set.
user_data/: user_data.py partition the training set based on the user ids, users/ stores the partitioned data.
Alibaba Dataset
taobao_data_process/: process raw Taobao log for centralized learning and split training set by user for federated learning.
Notes: The full industrial alibaba dataset cannot be released due to the restriction of Alibaba. We show the training set of a sample user in taobao_data_process/user_data_sample.
Train and Evaluate
Run the following command for training and evaluating:
bash run_multiprocess.shThe key implementation of FedSubAvg is in multiprocess_ps_functions.py.
Environments
Linux 4.4.0
Python 3.6.0
Numpy 1.16.0
TensorFlow 1.14.0
FlatBuffers 1.11
Matplotlib 3.0.0
Acknowledgement
We would like to sincerely thank Chaoyue Niu, Renjie Gu, and the edge-AI group in Taobao for their contributions to the project.