Federated learning (FL) enables a collaborative environment for training machine learning models without sharing training data between users. This is typically achieved by aggregating model gradients on a central server. Decentralized federated learning is a rising paradigm that enables users to collaboratively train machine learning models in a peer-to-peer manner, without the need for a central aggregation server. However, before applying decentralized FL in real-world use training environments, nodes that deviate from the FL process (Byzantine nodes) must be considered when selecting an aggregation function. Recent research has focused on Byzantine-robust aggregation for client-server or fully connected networks, but has not yet evaluated such aggregation schemes for complex topologies possible with decentralized FL. Thus, the need for empirical evidence of Byzantine robustness in differing network topologies is evident. This work investigates the effects of state-of-the-art Byzantine-robust aggregation methods in complex, large-scale network structures. We find that state-of-the-art Byzantine robust aggregation strategies are not resilient within large non-fully connected networks. As such, our findings point the field towards the development of topology-aware aggregation schemes, especially necessary within the context of large scale real-world deployment.

分布式联合学习是一种在用户之间进行训练机器学习模型而无需共享训练数据的协同环境，本研究调查了复杂的大规模网络结构中最先进的拜占庭容错聚合方法的效果，并发现当前的拜占庭容错聚合策略在大型非全连接网络中不具备弹性，因此需要开发具有拓扑感知能力的聚合方案来满足大规模实际部署的需求。

网络拓扑对分布式联邦学习中的拜占庭弹性的影响