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论文提出一种对抗鲁棒的广义编码计算（GCC）扩展框架，通过平滑正则化限制恶意worker输出的影响，并给出随对抗worker比例衰减的非渐近误差界，适用于分布式系统中的近似编码计算。
This paper proposes a manifold-aware encoding strategy for general coded computing that exploits the intrinsic low-dimensional geometry of high-dimensional data to improve straggler resilience in distributed systems. Experiments show reduced mean squared recovery error in neural network inference and polynomial evaluation tasks.
This paper introduces General Coded Computing (GCC), a learning-theoretic framework for mitigating stragglers in distributed computing systems, providing theoretical performance guarantees and experimental validation on deep neural networks.