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arXiv 2602.04810cs.ITmath.IT
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编码游戏用于向量值计算

Game of Coding for Vector-Valued Computations

University of Minnesota, Twin Cities(明尼苏达大学双城分校)

Hanzaleh Akbari Nodehi, Parsa Moradi, Soheil Mohajer, Mohammad Ali Maddah-Ali

AI总结:

本文提出一种扩展的编码游戏框架,用于多维欧几里得空间中的向量计算,研究了双重复代码在对抗性多数情况下玩家的均衡策略。

摘要:

传统编码理论仅在少数符号被恶意篡改时保证有效解码。相比之下,编码游戏框架在存在恶意多数的情况下也能保证可靠解码。这一框架受新兴无许可应用的启发,特别是去中心化机器学习(DeML),其中计算任务被外包给外部志愿者节点,这些节点大多理性且追求奖励。先前研究分析了标量设置下的编码游戏。由于大多数机器学习计算结果是向量(例如计算机器学习模型的损失梯度),本文将框架扩展到一般多维欧几里得空间。作为第一步,本文研究了一种双重复代码,其中至少一个节点受理性对抗者控制,并完全表征了玩家的均衡和最优策略。与标量情况类似,这一结果是解决更一般场景的基础。

英文摘要:

Traditional coding theory guarantees valid decoding only if a minority of symbols are adversarially manipulated. In contrast, the game of coding framework ensures reliable decoding, even in the presence of an adversarial majority. This formulation is motivated by emerging permissionless applications, particularly decentralized machine learning (DeML), where computation tasks are outsourced to external volunteer nodes that are predominantly rational and reward-seeking. Prior investigations have analyzed the game of coding in the scalar setting. Since the results of most major computations in machine learning are vectors (e.g., computing the gradient of the loss for a machine learning model), we extend the framework in this paper to the general multi-dimensional Euclidean space. As a first, yet fundamental step, in this paper, we study a two-repetition code in which at least one node is controlled by a rational adversary, and we fully characterize the equilibrium and the optimal strategies of the players. Similar to the scalar case, this result serves as a cornerstone for addressing more general scenarios.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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