DS & ML

🙌🏼7 Platforms of Federated ML
Federated learning is also referred to as collaborative because ML models are trained on multiple decentralized edge devices or servers containing local data samples without exchanging them. This approach differs from traditional centralized ML methods, where all local datasets are uploaded to a single server, and from more classical decentralized approaches with the same distribution of local data. Today, federated learning is actively used in the defense industry, telecommunications, pharmaceuticals and IoT platforms.
Federated Machine Learning ideas were first introduced by Google in 2017 to improve mobile keyboard text prediction using machine learning models trained on data from multiple devices. In federated ML, models are trained on multiple local datasets on local nodes without explicit data exchange, but with periodic exchange of parameters, such as deep neural network weights and biases, between local nodes to create a common global model. Unlike distributed learning, which was originally aimed at parallelizing computations, federated learning is aimed at learning heterogeneous data sets. In federated ML, datasets are usually highly heterogeneous in size. And clients, i.e. end devices where local models are trained can be unreliable and more prone to failure than in distributed learning systems where the nodes are data centers with powerful computing capabilities. Therefore, in order to provide distributed computing and synchronization of its results, federated ML requires frequent data exchange between nodes.
Due to its architectural features, federated ML has a number of disadvantages:
• heterogeneity between different local datasets - each node has an error in relation to the general population, and sample sizes can vary significantly;
• temporal heterogeneity - the distribution of each local dataset changes over time;
• it is necessary to ensure the compatibility of the data set on all nodes;
• hiding training datasets is fraught with the risk of introducing vulnerabilities into the global model;
• lack of access to global training data makes it difficult to identify unwanted biases in training inputs;
• there is a risk of losing updates to local ML models due to failures at individual nodes, which may affect the global model.
Today, federated ML is supported by the following platforms:
• FATE (Federated AI Technology Enabler) https://fate.fedai.org/
• Substra https://www.substra.ai/
• Python libraries PySyft and PyGrid https://github.com/OpenMined/PySyft, https://github.com/OpenMined/PyGrid, https://github.com/OpenMined/pygrid-admin
• Open FL https://github.com/intel/openfl
• TensorFlow Federated (TFF) https://www.tensorflow.org/federated
• IBM Federated Learning https://ibmfl.mybluemix.net/
• NVIDIA CLARA https://developer.nvidia.com/clara

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An Industrial Grade
Federated Learning Framework
support federated learning architectures and secure computation of any machine learning algorithms

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