In many real-world situations, data is distributed across multiple self-interested agents. These agents can collaborate to build a machine learning model based on data from multiple agents, potentially reducing the error each experiences. However, sharing models in this way raises questions of fairness: to what extent can the error experienced by one agent be significantly lower than the error experienced by another agent in the same coalition? In this work, we consider two notions of fairness that each may be appropriate in different circumstances: "egalitarian fairness" (which aims to bound how dissimilar error rates can be) and "proportional fairness" (which aims to reward players for contributing more data). We similarly consider two common methods of model aggregation, one where a single model is created for all agents (uniform), and one where an individualized model is created for each agent. For egalitarian fairness, we obtain a tight multiplicative bound on how widely error rates can diverge between agents collaborating (which holds for both aggregation methods). For proportional fairness, we show that the individualized aggregation method always gives a small player error that is upper bounded by proportionality. For uniform aggregation, we show that this upper bound is guaranteed for any individually rational coalition (where no player wishes to leave to do local learning).
翻译:在许多现实世界中, 数据分布于多个自我感兴趣的代理商之间。 这些代理商可以合作建立一个基于多个代理商的数据的机器学习模型, 有可能减少每个经验的错误。 但是, 以这种方式共享模型会引起公平问题: 一个代理商经历的错误在多大程度上会大大低于同一联盟中另一个代理商所经历的错误? 在这项工作中, 我们考虑两种公平概念, 每一种可能在不同情况下都适合两种公平概念 : “ 利他性公平 ” ( 目的是限制不同错误率如何不同 ) 和 “ 相称性公平 ” ( 目的是奖励行为者提供更多数据 ) 。 我们同样考虑两种共同的模型聚合方法, 一种是为所有代理商创建单一模型( 统一模式 ), 另一种是为每个代理商创建个化模型。 为了平等性公平, 我们得到了一个紧密的重复性约束, 在于代理商之间合作( 两种组合方法都支持两种方法 ) 。 关于比例性公平性, 我们表明个化合并方法总是给一个小的玩家错误, 最高受相称性约束 。 关于统一合并, 我们证明这个上界限是保证任何个体理性联盟( 学习玩家) 。</s>