GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework
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| Format: | Preprint |
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2025
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| _version_ | 1866917553867063296 |
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| author | Belal, Yacine Maouche, Mohamed Mokhtar, Sonia Ben |
| author_facet | Belal, Yacine Maouche, Mohamed Mokhtar, Sonia Ben |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_17471 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework Belal, Yacine Maouche, Mohamed Mokhtar, Sonia Ben Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing 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. |
| title | GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework |
| topic | Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.17471 |