  In this work we study the problem of Byzantine-robust learning when data
among clients is heterogeneous. We focus on poisoning attacks targeting the
convergence of SGD. Although this problem has received great attention; the
main Byzantine defenses rely on the IID assumption causing them to fail when
data distribution is non-IID even with no attack. We propose the use of Linear
Scalarization (LS) as an enhancing method to enable current defenses to
circumvent Byzantine attacks in the non-IID setting. The LS method is based on
the incorporation of a trade-off vector that penalizes the suspected malicious
clients. Empirical analysis corroborates that the proposed LS variants are
viable in the IID setting. For mild to strong non-IID data splits, LS is either
comparable or outperforming current approaches under state-of-the-art Byzantine
attack scenarios.
