Synchronous algorithms

Various synchronous global model update methods @ FL server

We have implemented various synchronous global model update methods taken place at the FL server given local model parameters received from clients, which are

• ServerFedAvg : averaging local model parameters to update global model parameters

• ServerFedAvgMomentum : ServerFedAvg with a momentum

• ServerFedAdagrad : use of the adaptive gradient (Adagrad) algorithm for a global update at a server

• ServerFedAdam : use of the adaptive moment estimation (Adam) algorithm for a global update

• ServerFedYogi : use of the Yogi algorithm for a global update

One can set which algorithm to use by setting servername in cfg.fed (e.g., cfg.fed.servername='ServerFedAvgMomentum'). One can also configure the hyperparameters for each algorithm, as shown in appfl/config/federated.py.

configurations of synchronous global update methods

    type: str = "federated"
    servername: str = "ServerFedAvg"
    clientname: str = "ClientOptim"
    args: DictConfig = OmegaConf.create(
        {
            ## Server update
            "server_learning_rate": 0.01,
            "server_adapt_param": 0.001,
            "server_momentum_param_1": 0.9,
            "server_momentum_param_2": 0.99,

Roughly speaking, the global update is done as follow:

global_model_parameter += (server_learning_rate * m) / ( sqrt(v) + server_adapt_param)

where

server_learning_rate: learning rate for the global update

server_adapt_param: adaptivity parameter

m: momentum

• m = server_momentum_param_1 * m + (1- server_momentum_param_1) * PseudoGrad

• PseudoGrad : pseudo gradient obtained by averaging differences of global and local model parameters

v: variance

• For ServerFedAdagrad:

  v = v + (PseudoGrad)^2

• For ServerFedAdam:

  v = server_momentum_param_2 * v + (1- server_momentum_param_2)* (PseudoGrad)^2

• For ServerFedYogi:

  v = v - (1 - server_momentum_param_2 )* (PseudoGrad)^2 sign(v - (PseudoGrad)^2)

See the following paper for more details on the above global update techniques:

Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S. and McMahan, H.B., 2020. Adaptive federated optimization. arXiv preprint arXiv:2003.00295