# FLaMAS: Federated Learning Based on a SPADE [PERSON]
## 2 Related Work  
There has been a huge increase in the number of mobile devices that can be used as providers of different kinds of data (e.g., sensory data, such as the global positioning system (GPS), accelerometers, any kind of sensor data, and even images).This data can be provided to deep learning processes to be used in intelligent applications. However, this idea has two very important problems associated with it--one is related to data privacy (as the mobile device has to communicate all the data to the server), while the other is related to bandwidth limitations. So, it is more appropriate only to have such data available on the device. That is the idea behind federated learning, as defined by Google in [6]; that is, a learning algorithm that benefits by sharing models of trained data with no central model. Thus, the learning is produced by a federation of _clients_ that are coordinated by a _server_. In traditional machine learning, data is obtained from different sources or devices and is sent to the server, where it is used to train the machine learning model. So, this is a _centralized learning_ model. In _federated learning_, the training is made in a distributed and edge fashion. So, the model training is carried out in the same device where the input data is obtained. This enables the use of sensible data for training, as data privacy is preserved by not sharing the data, only the trained model. Federated learning has not been the only approach to distributed learning preserving data privacy proposed in recent years. Another interesting approach is, for instance, _MIT split learning_ ([[https://splitlearning.github.io](https://splitlearning.github.io)]([https://splitlearning.github.io](https://splitlearning.github.io)) (accessed on 28 February 2022)) [12], where, in its simplest configuration, a deep learning network is split between two entities; the first computes the data until the cut layer, and the output of this layer (_smashed data_) is sent to the other entity for computation to be continued through the network. This can be performed for both training and execution. In this way, the privacy of the input data is preserved. One of the main open issues of this technique is parallelization over all IoT devices. In recent years, many studies have been undertaken to progress federated learning. In this respect, we have identified several review papers that have analyzed progress made in this area, such as of [7; 8], which provide an overview of existing developments and approaches and outline a number of future research directions. These papers summarize some applications in federated environments and discuss areas of development with considerable potential for the application of federated learning. In [13; 14], the authors classify federated learning research in terms of possible new designs, applications, and challenges. These reviews discuss the unique properties and challenges of federated learning compared with traditional distributed data center computing. In addition, they examine several open problems worthy of future research effort. One of the areas most closely related to federated learning is the IoT area. This is because, in most of the proposals, the learning occurs at the edge and, more specifically, in devices that directly perform the sensing of the data. The authors of [15] provide a review and comparative analysis of different existing open source federated learning frameworks. In addition, the paper considers their applicability in IoT systems, taking into account aspects such as analysis capabilities, accuracy, deployment, development, and performance. Another interesting review is provided in [16], where the authors analyze recent advances in federated learning that can enable IoT applications. The paper introduces a number of metrics, such as sparsification, robustness, quantization, scalability, security and privacy, to compare the analyzed proposals.