Low-latency distributed inference at the network edge using rateless codes

Paper i proceeding, 2021

We propose a coding scheme for low-latency distributed inference at the network edge that combines a rateless code with an irregular-repetition code. The rateless code provides robustness against straggling servers and serves the purpose of reducing the computation latency, while the irregular-repetition code provides spatial diversity to reduce the communication latency. We show that the proposed scheme yields significantly lower latency than a scheme based on maximum distance separable codes recently proposed by Zhang and Simeone.