[CIKM 2022]FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning

Abstract

Robustness is becoming another important challenge of federated learning in that the data collection process in each client is naturally accompanied by noisy labels. However, it is far more complex and challenging owing to varying levels of data heterogeneity and noise over clients, which exacerbates the client-to-client performance discrepancy. In this work, we propose a robust federated learning method called FedRN, which exploits k-reliable neighbors with high data expertise and similarity. Our method helps mitigate the gap between low- and high-performance clients by training with only a selected set of clean examples, identified by emsembled mixture models. We demonstrate the superiority of FedRN via extensive evaluations on three real-world or synthetic benchmark datasets. Compared with existing robust training methods, the results show that FedRN significantly improves the test accuracy in the presence of noisy labels.

Our main contributions are summarized as follows:

• To the best of our knowledge, this is the first work to present the problem of client-to-client performance discrepancy, which worsens considerably in the presence of noisy labels for federated learning.
• FedRN remarkably improves the overall robustness without much performance discrepancy owing to the use of k-reliable neighbors.
• FedRN significantly outperforms state-of-the art methods on three real-world or synthetic benchmark datasets with varying levels of data heterogeneity and label noise.

Installation

Please check below requirements and install packages from requirements.txt.

$ pip install --upgrade pip
$ pip install -r requirements.txt

Usage

The following command is an symmetric 0.0-0.4 and mixed 0.0-0.4 example of running the code.

# symmetric 0.0-0.4
python main_fed_LNL.py \
--dataset cifar10 \
--model cnn4conv \
--epochs 500 \
--noise_type_lst symmetric \
--noise_group_num 100  \
--group_noise_rate 0.0 0.4 \
--num_neighbors 2 \
--method fedrn

# mixed 0.0-0.4
python main_fed_LNL.py \
--dataset cifar10 \
--model cnn4conv \
--epochs 500 \
--noise_type_lst symmetric pairflip\
--noise_group_num 50 50  \
--group_noise_rate 0.0 0.4 \
--num_neighbors 2 \
--method fedrn

Parameters for learning

Parameter	Description
model	The model architecture. default = cnn4conv.
dataset	Dataset to use. Options: cifar10, cifar100. default = cifar10.
lr	Learning rate for the local models, default = 0.01.
momentum	SGD momentum, default = 0.5.
epochs	The total number of communication roudns, default = 500.

Parameters for federated learning

Parameter	Description
local_bs	Local batch size, default = 50.
loca_ep	Number of local update epochs, default = 5.
num_users	Number of users, Default = 100.
frac	The fraction of participating cleints, default = 0.1.
partition	The partition way for Non-IID. Options: shard, dirichlet, default = shard
num_shards	The number of total shards, default = 200.
dd_alpha	The concentration parameter alpha for Dirichlet distribution, default = 0.5.

Parameters for noisy label

Parameter	Description
noise_type_lst	Noisy type list.
noisy_group_num	Number of clients corresponding to noisy type.
group_noise_rate	The noise rate corresponding to the noisy group. It increases linearly from 0.0 to noise rate for each group.

Parameters for FedRN

Parameter	Description
num_neighbors	The number of reliable neighbors, default = 2
w_alpha	The hyperparameter controlling the contribution of expertise and data similiarity.

Please check run.sh for commands for various data and noisy label scenarios.

Experimental Result