# AlgoZoo

FederatedScope has built in various advanced federated learning algorithms. All of them are implemented as plug-ins, which are detachable and combinable.

• Detachable: only the activated code will participate in the operation,
• Combinable: different algorithms can be combined.

In this tutorial, you will learn the buildin algorithms, and how to implement a new federated algorithm in FederatedScope.

## Buildin Methods

### Distributed Optimization Methods

To tackle the challenge of statistical heterogeneity, we implement the following distributed optimization methods: FedAvg (Default), FedProx and FedOpt. Set the parameter cfg.{METHOD_NAME}.useas True to call them.

#### FedAvg

FedAvg [1] is a basic distributed optimization method in federated learning. During federated training, it broadcasts the initialized model to all clients, and aggregates the updated weights collected from several clients. FederatedScope implements it with a fedavg aggregator. More details can be found in federatedscope/core/aggregator.py.

We provide some evaluation results for fedavg on different tasks as follows.

Logistic regression Synthetic 68.36
Image classification FEMNIST 84.93
Next-character Prediction Shakespeare 43.80

To reproduce the results, the running scripts are listed as follows.

# logistic regression
python federatedscope/main.py --cfg federatedscope/nlp/baseline/fedavg_lr_on_synthetic.yaml

# image classification on femnist
python federatedscope/main.py --cfg federatedscope/cv/baseline/fedavg_convnet2_on_femnist.yaml

# next-character prediction on Shackespeare
python federatedscope/main.py --cfg federatedscope/nlp/baseline/fedavg_lstm_on_shakespeare.yaml


#### FedOpt

FedOpt [2] is an advanced distributed optimization method in federated learning. Compare with FedAvg, it permits the server to update weights rather than simply averaging the collected weights. More details can be found in federatedscope/core/aggregator.py.

Similar with FedAvg, we perform some evaluation of FedAvg on different tasks.

Task Data Learning rate (Server) Accuracy(%)
Logistic regression Synthetic 0.5 68.32
Image classification FEMNIST 1.0 84.92
Next-character Prediction Shakespeare 0.5 47.39

#### FedProx

FedProx [3] is designed to solve the problem of heterogeneity, which updates model with a proximal regularizer. FederatedScope provides build-in FedProx implementation and it can easily be combined with other algorithms. More details can be found in federatedscope/core/trainer/flpackage/core/trainers/trainer_fedprox.py.

The evaluation results are presented as follows.

Task Data $\mu$ Accuracy(%)
Logistic regression Synthetic 0.1 68.36
Image classification FEMNIST 0.01 84.77
Next-character Prediction Shakespeare 0.01 47.85

### Personalization Methods

#### FedBN

FedBN [4] is a simple yet effective approach to address feature shift non-iid challenge, in which the client BN parameters are trained locally, without communication and aggregation via server. FederatedScope provides simple configuration to implement FedBN and other variants that need to keep parameters of some model sub-modules local.

We provide some evaluation results for FedBN on different tasks as follows, in which the models contain batch normalization. Complete results, config files and running scripts can be found in scripts/personalization_exp_scripts/fedbn.

Image classification FEMNIST 85.48

#### pFedMe

pFedMe [5]  is an effective pFL approach to address data heterogeneity, in which
the personalized model and global model are decoupled with Moreau envelops. FederatedScope implements pFedMe in federatedscope/core/trainers/trainer_pFedMe.py and ServerClientsInterpolateAggregator in federatedscope/core/aggregator.py.

We provide some evaluation results for pFedMe on different tasks as follows. Complete results, config files and running scripts can be found in scripts/personalization_exp_scripts/pfedme.

Logistic regression Synthetic 68.73
Image classification FEMNIST 87.65
Next-character Prediction Shakespeare 37.40

#### Ditto

Ditto [6] is a SOTA pFL approach that improves fairness and robustness of FL via training local personalized model and global model simultaneously, in which the local model update is based on regularization to global model parameters. FederatedScope provides built-in Ditto implementation and users can easily extend to other pFL methods by re-using the model-para regularization. More details can be found in federatedscope/core/trainers/trainer_Ditto.py.

We provide some evaluation results for Ditto on different tasks as follows. Complete results, config files and running scripts can be found in scripts/personalization_exp_scripts/ditto.

Logistic regression Synthetic 69.67
Image classification FEMNIST 86.61
Next-character Prediction Shakespeare 45.14

#### FedEM

FedEM [7] is a SOTA pFL approach that assumes local data distribution is a mixture of unknown underlying distributions, and correspondingly learn a mixture of multiple internal models with Expectation-Maximization learning. FederatedScope provides built-in FedEM implementation and users can easily extends to other multi-model pFL methods based on this example. More details can be found in federatedscope/core/trainers/trainer_FedEM.py.

We provide some evaluation results for FedBN on different tasks as follows. Complete results, config files and running scripts can be found in scripts/personalization_exp_scripts/fedem.

Logistic regression Synthetic 68.80
Image classification FEMNIST 84.79
Next-character Prediction Shakespeare 48.06

## Implementation

### Preliminary

Before implementing a new federated algorithm, you need to realize the structure of Trainer and Context. If you already have the knowledge about them, in this part we’ll learn how to add an algorithm in FederatedScope.

In FederatedScope, there are three steps to implement a new federated algorithm:

• Prepare parameters: figure out the parameters required by your algorithm, and fill them into the Context
• Prepare hook functions: split your algorithm into several functions according to their insert positions within Trainer/Server,
• Assemble algorithm: create a warp function to assemble your algorithm before create the trainer object.

### Example (Fedprox)

Let’s take FedProx as an example to show how to implement a new federated algorithm.

#### Prepare parameters

First, FedProx requires to set proximal regularizer and its factor ctx.regularizer.mu.

# ------------------------------------------------------------------------ #
# Init variables for FedProx algorithm
# ------------------------------------------------------------------------ #
def init_fedprox_ctx(base_trainer):
ctx = base_trainer.ctx
cfg = base_trainer.cfg

cfg.regularizer.type = 'proximal_regularizer'
cfg.regularizer.mu = cfg.fedprox.mu

from federatedscope.core.auxiliaries.regularizer_builder import get_regularizer
ctx.regularizer = get_regularizer(cfg.regularizer.type)


#### Prepare Hook Functions

During training,FredProxrequires to record the initalized weights before local updating. Therefore, we create two hook functions to maintain the initialized weights.

• record_initialization: record initialized weights, and
• del_initialization: delete initialized weights to avoid memory leakage
# ------------------------------------------------------------------------ #
# Additional functions for FedProx algorithm
# ------------------------------------------------------------------------ #
def record_initialization(ctx):
ctx.weight_init = deepcopy(
[_.data.detach() for _ in ctx.model.parameters()])

def del_initialization(ctx):
ctx.weight_init = None


#### Assemble algorithm

After preparing parameters and hook functions, we assemble FedProx within the function wrap_fedprox_trainer in two steps:

• initialize parameters (call functioninit_fedprox_ctx)
• register hook functions for the given trainer
def wrap_fedprox_trainer(
base_trainer: Type[GeneralTrainer]) -> Type[GeneralTrainer]:
"""Implementation of fedprox refer to Federated Optimization in Heterogeneous Networks [Tian Li, et al., 2020]
(https://proceedings.mlsys.org/paper/2020/file/38af86134b65d0f10fe33d30dd76442e-Paper.pdf)

"""

# ---------------- attribute-level plug-in -----------------------
init_fedprox_ctx(base_trainer)

# ---------------- action-level plug-in -----------------------
base_trainer.register_hook_in_train(new_hook=record_initialization,
trigger='on_fit_start',
insert_pos=-1)

base_trainer.register_hook_in_eval(new_hook=record_initialization,
trigger='on_fit_start',
insert_pos=-1)

base_trainer.register_hook_in_train(new_hook=del_initialization,
trigger='on_fit_end',
insert_pos=-1)

base_trainer.register_hook_in_eval(new_hook=del_initialization,
trigger='on_fit_end',
insert_pos=-1)

return base_trainer


Finally, add FedProx into the function get_trainer(federatedscope/core/auxiliaries/trainer_builder.py).

def get_trainer(model=None,
data=None,
device=None,
config=None,
only_for_eval=False,
is_attacker=False):

trainer = ...

# fed algorithm plug-in
if config.fedprox.use:
from federatedscope.core.trainers.trainer_fedprox import wrap_fedprox_trainer
trainer = wrap_fedprox_trainer(trainer)


## Run an Example

Generally, build-in algorithms are called by setting the parameter \$cfg.{METHOD_NAME}.use as True. For more infomation about their parameters, you can refer to federatedscope/config.py. Similarily, taking FedProx as an exmple, its parameters in federatedscope/core/config.py are

# ------------------------------------------------------------------------ #
# fedprox related options
# ------------------------------------------------------------------------ #
cfg.fedprox = CN()

cfg.fedprox.use = True		# Whether to use fedprox
cfg.fedprox.mu = 0. 		# The regularizer factor within fedprox


You can call FedProx by the following command in the terminal

python federatedscope/main.py --cfg {YOUR_CONFIG_FILE} fedprox.use True fedprox.mu 0.1


More example scripts are refer toscripts/example_configs/.

### Note

Most combinations of the buildin methods have been tested. When implementing your own methods, it is suggested to carefully check the code to avoid conflicts (e.g. duplication of variables).

## References

[1] McMahan B, Moore E, Ramage D, et al. “Communication-efficient learning of deep networks from decentralized data”. International Conference on Artificial Intelligence and Statistics, 2017.

[2] Reddi S J, Charles Z, Zaheer M, et al. “Adaptive federated optimization”. Intertional Conference on Learning Representations, 2021.

[3] Li T, Sahu A K, Zaheer M, et al. “Federated optimization in heterogeneous networks”. Proceedings of Machine Learning and Systems, 2020.

[4] Li X, Jiang M, Zhang X, et al. “Fedbn: Federated learning on non-iid features via local batch normalization”. arXiv preprint arXiv:2102.07623 (2021).

[5] Dinh C T, Tran N H, and Nguyen T D. “Personalized federated learning with moreau envelopes”. Advances in Neural Information Processing Systems 33 (2020): 21394-21405.

[6] Li T, Hu S, Beirami A, et al. “Ditto: Fair and robust federated learning through personalization”. International Conference on Machine Learning. PMLR, 2021.

[7] Marfoq O, Neglia G, Bellet A, et al. “Federated multi-task learning under a mixture of distributions”. Advances in Neural Information Processing Systems 34 (2021).

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