Gossip Learning (GL) is a decentralized learning paradigm where users
iteratively exchange and aggregate models with a small set of neighboring
peers. Recent GL approaches rely on dynamic communication graphs built and
maintained using Random Peer Sampling (RPS) protocols. Thanks to graph
dynamics, GL can achieve fast convergence even over extremely sparse
topologies. Jedoch, the robustness of GL over dy- namic graphs to Byzantine
(model poisoning) attacks remains unaddressed especially when Byzantine nodes
attack the RPS protocol to scale up model poisoning. We address this issue by
introducing GRANITE, a framework for robust learning over sparse, dynamic
graphs in the presence of a fraction of Byzantine nodes. GRANITE relies on two
key components (ich) a History-aware Byzantine-resilient Peer Sampling protocol
(HaPS), which tracks previously encountered identifiers to reduce adversarial
influence over time, Und (ii) an Adaptive Probabilistic Threshold (APT), which
leverages an estimate of Byzantine presence to set aggregation thresholds with
formal guarantees. Empirical results confirm that GRANITE maintains convergence
with up to 30% Byzantine nodes, improves learning speed via adaptive filtering
of poisoned models and obtains these results in up to 9 times sparser graphs
than dictated by current theory.

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