pooling: Local learning rate: Those parameters have to be optimized depending on the constraints of the machine learning application (e.g., available computing power, available memory, bandwidth). For instance, stochastically choosing a limited fraction of nodes for each iteration diminishes computing cost and may prevent overfitting, in the same way that stochastic gradient descent can reduce overfitting. Technical limitations Federated learning requires frequent communication between nodes during the learning process. Thus, it requires not only enough local computing power and memory, but also high bandwidth connections to be able to exchange parameters of the machine learning model. However, the technology also avoids data communication, which can require significant resources before starting centralized machine learning. Nevertheless, the devices typically employed in federated learning are communication-constrained, for example IoT devices or smartphones are generally connected to Wi-Fi networks, thus, even if the models are commonly less expensive to be transmitted compared to raw data, federated learning mechanisms may not be suitable in their general form. Federated learning raises several statistical challenges: Heterogeneity between the different local datasets: each node may have some bias with respect to the general population, and the size of the datasets may vary significantly; Temporal heterogeneity: each local dataset's distribution may vary with time; Interoperability of each node's dataset is a prerequisite; Each node's dataset may require regular curations; Hiding training data might allow attackers to inject backdoors into the global model; Lack of access to global training data makes it harder to identify unwanted biases entering the training e.g. age, gender, sexual orientation; Partial or total loss of model updates due to node failures affecting the global model; Lack of annotations or labels on the client side. Heterogeneity between the processing platform Federated learning variations In this section, the notation of the paper published by H. Brendan McMahan and al. in 2017 is followed. To describe the federated strategies, let us introduce some notations: : total number of clients; : index of clients; : number of data samples available during training for client ; : model's weight vector on client , at the federated round ; : loss function for weights and batch ; : number of local updates; Federated stochastic gradient descent (FedSGD) Deep learning training mainly relies on variants of stochastic gradient descent, where gradients are computed on a random subset of the total dataset and then used to make one step of the gradient descent.