Decentralized federated learning (DFL) enables clients (e.g., hospitals and
banks) to jointly train machine learning models without a central orchestration
server. In each global training round, each client trains a local model on its
own training data and then they exchange local models for aggregation. In this
work, we propose SelfishAttack, a new family of attacks to DFL. In
SelfishAttack, a set of selfish clients aim to achieve competitive advantages
over the remaining non-selfish ones, i.e., the final learnt local models of the
selfish clients are more accurate than those of the non-selfish ones. Towards
this goal, the selfish clients send carefully crafted local models to each
remaining non-selfish one in each global training round. We formulate finding
such local models as an optimization problem and propose methods to solve it
when DFL uses different aggregation rules. Theoretically, we show that our
methods find the optimal solutions to the optimization problem. Empirically, we
show that SelfishAttack successfully increases the accuracy gap (i.e.,
competitive advantage) between the final learnt local models of selfish clients
and those of non-selfish ones. Moreover, SelfishAttack achieves larger accuracy
gaps than poisoning attacks when extended to increase competitive advantages.

分散联邦学习（DFL）使得客户（如医院和银行）在没有中央编排服务器的情况下共同训练机器学习模型。在每个全局训练轮次中，每个客户在自己的训练数据上训练本地模型，然后交换本地模型进行聚合。在本文中，我们提出了 SelfishAttack，一种新的针对 DFL 的攻击方法。SelfishAttack 中，一组自私的客户旨在比其他非自私的客户获得竞争优势，即自私客户的最终学习本地模型比非自私客户的更准确。为实现这一目标，自私客户在每个全局训练轮次中向剩余的非自私客户发送精心制作的本地模型。我们将寻找这种本地模型视为一个优化问题，并提出了解决 DFL 使用不同聚合规则时的方法。从理论上讲，我们证明了我们的方法可以找到优化问题的最优解。实证上，我们展示了 SelfishAttack 成功增加了自私客户最终学习的本地模型与非自私客户之间的准确性差距（竞争优势）。此外，当扩展以增加竞争优势时，SelfishAttack 比毒化攻击实现了更大的准确性差距。