Faults are inevitable in a very large scale distributed computing system such as cloud computing. The size of distributed computing is enlarging drastically due to the advent of the Internet of things (IoT). Faults occur frequently at any working node and cause the partial or complete failure of the cloud applications. Implementing fault resilient systems and securing cloud systems have become key challenging problems in recent years. A novel model with federated learning (FL) is analyzed and proposed to deal with these challenges. Federated learning, a special kind of distributed deep learning, works in collaboration with the distributed computing machines. A Federated learning model can be deployed on multiple clusters of computing nodes. One of the features of distributed computing is that it is growing drastically towards horizontal and vertical directions. The Federated learning model is deployed on both horizontal and vertical scaling. FL deployed with distributed deep learning can identify, recognize, and resolve the faults to great extent.
To reduce the adverse effects of faults, machine learning (ML) especially the federated learning (FL) approach is deployed. Federated learning is a distributed and decentralized paradigm of protocols. The Federated learning approach is well suited for a distributed system because a set of worker machines (or nodes) can train the local models. Different chunks of datasets are distributed among the worker nodes or third parties. Here sections of a dataset are not shared by the working computational nodes. Thus federated learning is also the most significant model for achieving data privacy and data security in addition to fault tolerance. The existing FL approaches highlight optimizing only one dimension of the target space. The proposed methods can reduce communication costs and improve the efficiency of distributed computing. Federate deep learning (FDL) method minimizes the adverse effects with an improved convergence rate. This approach utilizes a weighted aggregation for accuracy improvement. FDL is capable to detect and diagnose the faults that occur frequently on end-user devices as well as on the edge. FDL is a novel communication efficient FL approach. It incorporates both synchronous and asynchronous arrangements. Federated learning (FL) is a multi-modal machine learning system that trains the algorithm among various distributed and decentralized edge devices that holds local datasets. The intelligent device such as PDAs, smart-phones, and desktops or tablets system has been scaling rapidly in recent years. Most of these devices are equipped with multiple sensors that allow them to produce and consume a huge amount of information. Distributed computing hierarchy consists of cloud, edge, and end-user devices. End-user devices train the local models and use local datasets. End device and clientâ€™s behavioral heterogeneity become the key cause of fault inclusion in cloud systems. The cloud system plays a major role in scaling big data.
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Department of Computer Science and IT, Faculty of Computer Applications & Information Technology and Sciences, AKS University, Satna, 485001, Madhya Pradesh, India