Survey Optimizing Reinforcement Learning, Federated Learning, and Computational Network Model Performance

Federated learning enables distributed collaborative machine learning without compromising data privacy. Federated learning is a novel way to improve computer network efficiency and flexibility, when performance is a top goal. This study examines how federated learning might improve computerised network performance by focusing on bandwidth, latency, and fault tolerance. The research proposes to create a federated learning model where nodes with their own datasets may enhance computer network performance. The federated learning approach allows nodes to artefact or learn from experience without losing data locality via data aggregation. This study addresses federated learning difficulties such communication overhead, data heterogeneity, and model convergence. We also provide plausible amelioration methods based on tests and simulations to determine federated learning's effectiveness in improving computer network performance. Simulations show that this novel method may increase network efficiency, flexibility, and resilience in dynamic and heterogeneous computer networks. After addressing potential applications in wireless sensor networks, edge computing, and Internet of Things systems, the article will recommend further research in this area. Federated learning may convert efficient, resilient, and secure computer networks, unlike the RL, FL Model, and Computational model.