Grad-FEC: Unequal Loss Protection of Deep Features in Collaborative Intelligence

• URL: http://arxiv.org/abs/2307.01846v1
• Date: Tue, 4 Jul 2023 17:49:46 GMT
• Title: Grad-FEC: Unequal Loss Protection of Deep Features in Collaborative Intelligence
• Authors: Korcan Uyanik, S. Faegheh Yeganli, Ivan V. Baji\'c
• Abstract summary: Collaborative intelligence (CI) involves dividing an artificial intelligence (AI) model into two parts: front-end, to be deployed on an edge device, and back-end, to be deployed in the cloud. The deep feature tensors produced by the front-end are transmitted to the cloud through a communication channel, which may be subject to packet loss. We propose a novel approach to enhance the resilience of the CI system in the presence of packet loss through Unequal Loss Protection (ULP)
• Score: 27.135997578218486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative intelligence (CI) involves dividing an artificial intelligence (AI) model into two parts: front-end, to be deployed on an edge device, and back-end, to be deployed in the cloud. The deep feature tensors produced by the front-end are transmitted to the cloud through a communication channel, which may be subject to packet loss. To address this issue, in this paper, we propose a novel approach to enhance the resilience of the CI system in the presence of packet loss through Unequal Loss Protection (ULP). The proposed ULP approach involves a feature importance estimator, which estimates the importance of feature packets produced by the front-end, and then selectively applies Forward Error Correction (FEC) codes to protect important packets. Experimental results demonstrate that the proposed approach can significantly improve the reliability and robustness of the CI system in the presence of packet loss.

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