Federated learning is typically considered a beneficial technology which allows multiple agents to collaborate with each other, improve the accuracy of their models, and solve problems which are otherwise too data-intensive / expensive to be solved individually. However, under the expectation that other agents will share their data, rational agents may be tempted to engage in detrimental behavior such as free-riding where they contribute no data but still enjoy an improved model. In this work, we propose a framework to analyze the behavior of such rational data generators. We first show how a naive scheme leads to catastrophic levels of free-riding where the benefits of data sharing are completely eroded. Then, using ideas from contract theory, we introduce accuracy shaping based mechanisms to maximize the amount of data generated by each agent. These provably prevent free-riding without needing any payment mechanism.
翻译:联邦学习通常被视为一种有益的技术,它使多个代理商能够相互协作,提高其模型的准确性,并解决那些在其他方面过于数据密集/费用昂贵的问题,无法单独解决。然而,由于预期其他代理商会分享数据,理性代理商可能会受到诱惑,从事有害行为,如在他们不提供数据但仍然享受改进模式的情况下,他们可以提供免费食用。在这项工作中,我们提出了一个框架来分析这种理性数据生成者的行为。我们首先展示一个天真的计划如何导致在数据共享的好处被完全侵蚀的情况下自由食用导致灾难性程度的自由食用。然后,我们利用合同理论的想法,引入基于准确制成的机制,以最大限度地增加每个代理商产生的数据数量。这些明显地防止了免费食用,而不需要任何支付机制。