# Strategy Library

Reference guide for available training strategies.

## FedAvg

Federated Averaging strategy implemented based on [https://arxiv.org/abs/1602.05629](https://arxiv.org/abs/1602.05629)

### Parameters

* fraction_fit (float, optional): Fraction of clients used during training. Defaults to 0.1. 
* fraction_eval (float, optional): Fraction of clients used during validation. Defaults to 0.1.
* min_fit_clients (int, optional): Minimum number of clients used during training. Defaults to 1.
* min_eval_clients (int, optional): Minimum number of clients used during validation. Defaults to 1. 
* min_available_clients (int, optional): Minimum number of total clients in the system. Defaults to 1. 
* eval_fn (Callable[[Weights], Optional[Tuple[float, float]]], optional): Function used for validation. Defaults to None. 
* on_fit_config_fn (Callable[[int], Dict[str, Scalar]], optional): Function used to configure training. Defaults to None.
* on_evaluate_config_fn (Callable[[int], Dict[str, Scalar]], optional): Function used to configure validation. Defaults to None.
* accept_failures (bool, optional): Whether or not accept rounds containing failures. Defaults to True. initial_parameters (Weights, optional): Initial global model parameters.

## FedAvgM

Federated Averaging with Momentum (FedAvgM) strategy h[ttps://arxiv.org/pdf/1909.06335.pdf](https://arxiv.org/pdf/1909.06335.pdf)

### Parameters

Uses the same parameters as `FedAvg` as well as the following: 

* initial_parameters (Weights, optional): Initial global model parameters. 
* server_learning_rate (float): Server-side learning rate used in server-side optimization. Defaults to 1.0, which is the same as the vanilla FedAvg 
* server_momentum (float): Server-side momentum factor used for FedAvgM. Defaults to 0.0. 
* nesterov (bool): Enables Nesterov momentum. Defaults to False.

## FedAdam

Adaptive Federated Optimization using Adam (FedAdam) [https://arxiv.org/abs/2003.00295](https://arxiv.org/abs/2003.00295)

### Parameters

Uses the same parameters as `FedAvg` as well as the following: 

* initial_parameters (Weights, optional): Initial global model parameters. 
* eta (float, optional): Server-side learning rate. Defaults to 1e-1. 
* beta_1 (float, optional): Momentum parameter. Defaults to 0.9. 
* beta_2 (float, optional): Second moment parameter. Defaults to 0.99. 
* tau (float, optional): Controls the degree of adaptability for the algorithm. Defaults to 1e-3.

## FedAdagrad

Adaptive Federated Optimization using Adagrad (FedAdagrad) strategy [https://arxiv.org/abs/2003.00295](https://arxiv.org/abs/2003.00295)

### Parameters

Uses the same parameters as `FedAvg` as well as the following: 

* initial_parameters (Weights, optional): Initial global model parameters. 
* eta (float, optional): Server-side learning rate. Defaults to 1e-1. 
* beta_1 (float, optional): Momentum parameter. Defaults to 0.0. Note that typical AdaGrad does not use momentum, thus usually beta_1 is kept 0.0 
* tau (float, optional): Controls the degree of adaptability for the algorithm. Defaults to 1e-3. Smaller tau means higher degree of adaptability of server-side learning rate.

## FedIRLS

A standalone implementation of the Iteratively Reweighted Least Squares (IRLS) algorithm. IRLS is a standard method for finding maximum likelihood estimates in GLMs, such as Logistic Regression, where the objective function is not directly solved via ordinary least squares.

### Parameters

* thresh (optional): controls the threshold for convergence. 

## FedYogi

Federated learning strategy using Yogi on server-side [https://arxiv.org/abs/2003.00295v5](https://arxiv.org/abs/2003.00295v5)

### Parameters

* initial_parameters (Weights, optional): Initial global model parameters. 
* eta (float, optional): Server-side learning rate. Defaults to 1e-1. 
* beta_1 (float, optional): Momentum parameter. Defaults to 0.9. 
* beta_2 (float, optional): Second moment parameter. Defaults to 0.99. 
* tau (float, optional): Controls the degree of adaptability for the algorithm. Defaults to 1e-3.