Gossip Learning (GL) is a decentralized learning paradigm where users iteratively exchange and aggregate models with a small set of neighboring peers. Recent approaches rely on dynamic communication graphs built using Random Peer Sampling (RPS) protocols which have been proven to accelerate convergence. However, we show that these approaches are vulnerable to a dual attack: Byzantine nodes can poison models and manipulate peer sampling to amplify their influence. We address this combination of threats with GRANITE, a framework for robust learning over sparse, dynamic graphs in the presence of Byzantine nodes. GRANITE accumulates knowledge about encountered node identifiers over time and dynamically adjusts local aggregation thresholds based on estimated Byzantine density in the neighbourhood of each node. We demonstrate that under GRANITE, the Byzantine presence in local neighborhoods exhibits an exponential decay. We further derive the robustness conditions of the graphs generated by GRANITE. Empirically, our results indicate that GRANITE converges within 5% of non-Byzantine accuracy under 30% Byzantines nodes, offers faster convergence and operates on graphs with up to 9x lower communication cost.

翻译：八卦学习（GL）是一种去中心化学习范式，用户通过与小部分邻居节点迭代交换并聚合模型。近期方法依赖基于随机对等采样（RPS）协议构建的动态通信图，已被证明可加速收敛。然而，我们表明这些方法易遭受双重攻击：拜占庭节点可毒化模型并操纵对等采样以放大其影响力。针对此类组合威胁，我们提出GRANITE框架，该框架能在稀疏动态图中实现拜占庭节点存在下的鲁棒学习。GRANITE随时间累积遭遇节点标识的知识，基于各节点邻域中估计的拜占庭密度动态调整局部聚合阈值。我们证明在GRANITE下，局部邻域中的拜占庭节点呈现指数级衰减，并进一步推导其生成图的鲁棒性条件。实验表明，在30%拜占庭节点比例下，GRANITE收敛精度与无拜占庭工况偏差小于5%，同时实现更快收敛，且运行于通信成本降低9倍的图上。