  Byzantine Fault Tolerance (BFT) is one of the most challenging problems in
Distributed Machine Learning (DML), defined as the resilience of a
fault-tolerant system in the presence of malicious components. Byzantine
failures are still difficult to deal with due to their unrestricted nature,
which results in the possibility of generating arbitrary data. Significant
research efforts are constantly being made to implement BFT in DML. Some recent
studies have considered various BFT approaches in DML. However, some aspects
are limited, such as the few approaches analyzed, and there is no
classification of the techniques used in the studied approaches. In this paper,
we present a survey of recent work surrounding BFT in DML, mainly in
first-order optimization methods, especially Stochastic Gradient Descent(SGD).
We highlight key techniques as well as fundamental approaches. We provide an
illustrative description of the techniques used in BFT in DML, with a proposed
classification of BFT approaches in the context of their fundamental
techniques. This classification is established on specific criteria such as
communication process, optimization method, and topology setting, which
characterize future work methods addressing open challenge
