Approximated Coded Computing: Towards Fast, Private and Secure Distributed Machine Learning
arxiv(2024)
Abstract
In a large-scale distributed machine learning system, coded computing has
attracted wide-spread attention since it can effectively alleviate the impact
of stragglers. However, several emerging problems greatly limit the performance
of coded distributed systems. Firstly, an existence of colluding workers who
collude results with each other leads to serious privacy leakage issues.
Secondly, there are few existing works considering security issues in data
transmission of distributed computing systems. Thirdly, the number of required
results for which need to wait increases with the degree of decoding functions.
In this paper, we design a secure and private approximated coded distributed
computing (SPACDC) scheme that deals with the above-mentioned problems
simultaneously. Our SPACDC scheme guarantees data security during the
transmission process using a new encryption algorithm based on elliptic curve
cryptography. Especially, the SPACDC scheme does not impose strict constraints
on the minimum number of results required to be waited for. An extensive
performance analysis is conducted to demonstrate the effectiveness of our
SPACDC scheme. Furthermore, we present a secure and private distributed
learning algorithm based on the SPACDC scheme, which can provide
information-theoretic privacy protection for training data. Our experiments
show that the SPACDC-based deep learning algorithm achieves a significant
speedup over the baseline approaches.
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