Competitive Advantage Attacks to Decentralized Federated Learning
Yuqi Jia Minghong Fang Neil Zhenqiang Gong
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.