GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework

GRANITE is a new Byzantine-resilient framework for decentralized gossip learning that addresses vulnerabilities in dynamic peer sampling protocols used in distributed machine learning. The system demonstrates resilience against coordinated attacks where malicious nodes both poison models and manipulate network topology, achieving near-optimal accuracy with up to 30% Byzantine nodes while reducing communication costs by 9x.

GRANITE addresses a critical vulnerability in decentralized learning systems where Byzantine nodes exploit dynamic gossip protocols through dual attacks—simultaneously poisoning model updates and manipulating peer sampling mechanisms to amplify their influence. This research reflects growing recognition that decentralized AI systems require robust Byzantine fault tolerance as they scale beyond trusted environments, particularly relevant for blockchain-based machine learning and distributed AI applications.

The framework's innovation lies in its dynamic threshold adjustment and accumulated knowledge of node identifiers, which causes Byzantine presence in neighborhoods to decay exponentially over time. This approach differs from static defenses by adapting to changing network conditions, making it more practical for real-world deployment where adversaries evolve tactics. The empirical results—maintaining 95% accuracy under 30% Byzantine nodes while reducing communication overhead—represent meaningful progress in making decentralized learning viable for production systems.

For the distributed AI and blockchain sectors, GRANITE's efficiency gains matter significantly. The 9x reduction in communication costs directly translates to lower computational overhead and network bandwidth requirements, making decentralized learning economically feasible for edge devices and resource-constrained networks. This has implications for DeFi systems relying on distributed model training, federated learning platforms, and decentralized autonomous organization governance mechanisms that depend on model consensus.

Future developments should focus on testing GRANITE against adaptive adversaries that specifically target its threshold adjustment mechanisms and on scaling the framework to larger, more heterogeneous networks with non-uniform peer distributions.

• →GRANITE prevents Byzantine nodes from manipulating both model updates and peer sampling through dynamic threshold adjustment based on accumulated node identity data
• →Framework achieves 95% accuracy retention with 30% Byzantine nodes while reducing communication costs by 9x compared to baseline approaches
• →Exponential decay of Byzantine influence in local neighborhoods provides strong theoretical guarantees for long-term robustness
• →Results demonstrate practical feasibility of Byzantine-resilient decentralized learning for resource-constrained and edge computing environments
• →Research addresses critical gap in security of dynamic gossip protocols used in distributed AI and blockchain systems