In recent years, federated learning has been embraced as an approach for bringing about collaboration across large populations of learning agents. However, little is known about how collaboration protocols should take agents' incentives into account when allocating individual resources for communal learning in order to maintain such collaborations. Inspired by game theoretic notions, this paper introduces a framework for incentive-aware learning and data sharing in federated learning. Our stable and envy-free equilibria capture notions of collaboration in the presence of agents interested in meeting their learning objectives while keeping their own sample collection burden low. For example, in an envy-free equilibrium, no agent would wish to swap their sampling burden with any other agent and in a stable equilibrium, no agent would wish to unilaterally reduce their sampling burden. In addition to formalizing this framework, our contributions include characterizing the structural properties of such equilibria, proving when they exist, and showing how they can be computed. Furthermore, we compare the sample complexity of incentive-aware collaboration with that of optimal collaboration when one ignores agents' incentives.

中文翻译：

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一对一，或全民所有：联合学习中的均衡和合作的最优性

近年来，联盟学习已被视为一种在众多学习代理之间实现协作的方法。但是，对于分配协议用于社区学习以维持此类协作的个人资源时，协作协议应如何考虑代理商的激励机制知之甚少。受游戏理论概念的启发，本文介绍了一种在联合学习中用于激励意识学习和数据共享的框架。我们的稳定和无羡慕的平衡体现了合作者在有兴趣满足他们的学习目标的同时保持他们自己的样本收集负担低的兴趣的合作理念。例如，在无羡慕的平衡中，没有任何代理人希望与其他任何代理人交换采样负担，并且在稳定的平衡中，没有代理商愿意单方面减轻他们的抽样负担。除了使这个框架正式化之外，我们的贡献还包括表征这种平衡的结构特性，证明它们何时存在以及如何计算它们。此外，当人们忽略代理人的激励时，我们将激励意识协作的样本复杂性与最优协作的样本复杂性进行了比较。