In distributed learning, a central server trains a model according to updates provided by nodes holding local data samples.In the presence of one Memorial Keychain Gift Box or more malicious servers sending incorrect information (a Byzantine adversary), standard algorithms for model training such as stochastic gradient descent (SGD) fail to converge.In this paper, we present a simplified convergence theory for the generic Byzantine Resilient SGD method originally proposed by Blanchard et AEG BPK842720M Electric Single Stainless Steel Pyrolytic Oven al.(2017) [3].Compared to the existing analysis, we shown convergence to a stationary point in expectation under standard assumptions on the (possibly nonconvex) objective function and flexible assumptions on the stochastic gradients.