The widespread adoption of large-scale machine learning models in recent
years highlights the need for distributed computing for efficiency and
scalability. This work introduces a novel distributed machine learning paradigm
-- \emph{consensus learning} -- which combines classical ensemble methods with
consensus protocols deployed in peer-to-peer systems. These algorithms consist
of two phases: first, participants develop their models and submit predictions
for any new data inputs; second, the individual predictions are used as inputs
for a communication phase, which is governed by a consensus protocol. Consensus
learning ensures user data privacy, while also inheriting the safety measures
against Byzantine attacks from the underlying consensus mechanism. We provide a
detailed theoretical analysis for a particular consensus protocol and compare
the performance of the consensus learning ensemble with centralised ensemble
learning algorithms. The discussion is supplemented by various numerical
simulations, which describe the robustness of the algorithms against Byzantine
participants.

该研究论文介绍了一种新颖的分布式机器学习范式 —— 共识学习，它将经典的集成方法与点对点系统中部署的共识协议相结合。该算法包括两个阶段：参与者开发模型并对任何新的数据输入提交预测；个体预测作为输入参与通信阶段，该阶段由共识协议控制。共识学习保证用户数据隐私，同时继承底层共识机制对拜占庭攻击的安全措施。我们对特定共识协议进行了详细的理论分析，并将共识学习集成与集中式集成学习算法的性能进行了比较。此外，通过各种数字模拟，描述了算法对拜占庭参与者的鲁棒性。