What applications are envisioned for this two-stage, latency-sensitive federated learning archi- tecture?
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The two-stage, latency-sensitive federated learning architecture has envisioned applications in various domains. One application is in the field of wireless networks, specifically for beam blockage prediction in sixth-generation (6G) wireless networks. This architecture utilizes multi-sensor data and advanced deep learning techniques to anticipate potential changes in the wireless network surroundings, achieving high predictive accuracy with reduced communication costs and latency . Another application is in the Businesses-to-Businesses scenarios, where a decentralized federated learning architecture is proposed using a consortium blockchain. This architecture ensures the quality of local models trained by participators and quantifies the actual delays in the system . Additionally, this architecture is applicable in the coexistence of privacy-sensitive and privacy-insensitive client-devices, where a hybrid centralized training and local training framework is proposed. It optimizes the overall latency for reaching federated learning convergence, considering client-devices' selections, training configurations, and bandwidth allocations . Furthermore, this architecture can be applied to accelerate federated learning enabled end-cloud two-tier computing over wireless networks, enhancing the minimum wireless link rate and achieving faster federated learning tasks .
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| The paper does not specifically mention the applications envisioned for this two-stage, latency-sensitive federated learning architecture. |
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