Enhancing Cloud Computing Performance Through Federated Learning Techniques
Keywords:
Cloud Computing, Federated Learning, Data Privacy, Decentralized Training, Edge Computing, Machine Learning, Distributed Computing, Security, Resource Optimization, Performance EnhancementAbstract
Cloud computing has revolutionized data processing, storage, and accessibility by providing scalable solutions to various businesses. However, processing efficiency, latency, and security issues have emerged due to the rising demand for cloud-based services. Federated learning (FL) has a lot of promise in improving cloud computing. It allows decentralized model training over distant devices while ensuring data privacy. This study assesses the possible advantages of federated learning, including better security, less bandwidth consumption, optimized resource allocation, and more efficient cloud computing. Integrating various FL designs into cloud systems is a topic of ongoing research. Vertical, horizontal, and hybrid FL are some of these designs. Case studies and experimental assessments show how FL may be used to reach the most potential cloud performance. This essay covers a lot of ground, including model convergence, heterogeneity, and communication overhead. Also included are potential areas for future research and any problems that may arise.
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References
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