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Article summary:
1. The article studies the resilience of distributed implementations of Stochastic Gradient Descent (SGD) to Byzantine failures.
2. Current approaches cannot tolerate a single Byzantine failure, so a new aggregation rule is proposed that satisfies a resilience property and is provably Byzantine-resilient.
3. Experimental evaluations of the proposed algorithm are reported on.
Article analysis:
The article provides an in-depth analysis of the resilience of distributed implementations of Stochastic Gradient Descent (SGD) to Byzantine failures, and proposes a new aggregation rule that is provably resilient to such failures. The authors provide evidence for their claims through experimental evaluations, which adds to the trustworthiness and reliability of the article. However, there are some potential biases in the article that should be noted. For example, the authors focus solely on SGD and do not consider other machine learning algorithms or frameworks, which could lead to one-sided reporting or unsupported claims about SGD's superiority over other methods. Additionally, there is no discussion of possible risks associated with using SGD in distributed systems, nor any exploration of counterarguments or alternative solutions to the problem at hand. Finally, it is unclear whether all sides have been presented equally in terms of potential benefits and drawbacks associated with using SGD in distributed systems.