Implemented FL Algorithms

Various global model update methods @ server

We have implemented various global model update methods taken place at the 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 (e.g., cfg.fed.servername=ServerFedAvg). One can configure each algorithm (e.g., selecting hyperparameters) in

configuration of the 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,
            ## Clients optimizer

Roughly speaking, the global update is done as follow:

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


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