这个框架用Gossip历史做共识，不用区块链，训练质量不差还能抗恶意节点，适合想搞去中心化联邦学习的人看看。
gspDAG-FL提出了一种去中心化联邦学习框架，利用Gossip协议中的历史消息达成共识，无需中央服务器或区块链。全节点通过收集事件证书和接受性证明，构建紧凑拓扑有向无环图（DAG），并运行Hashgraph风格的虚拟投票实现最终性。该框架在MNIST分类和Penn Treebank语言建模任务上测试，网络规模N=100，学习质量接近验证型账本FL，同时减少了协调瓶颈并提高了吞吐量，在混合拜占庭和懒惰节点参与下保持了高无效来源检测率。
Secure Decentralized Federated Learning via Gossip and Virtual Voting
Decentralized federated learning (DFL) removes the central server by letting nodes exchange model updates through peer-to-peer gossip, but existing gossip-based methods often lack provenance finality and resilience to Byzantine or lazy participants. Ledger-assisted federated learning (FL) improves auditability, yet blockchains, shards, or settlement committees can reintroduce global coordination costs that conflict with DFL locality. This paper proposes \emph{gspDAG-FL}, a secure DFL framework that derives consensus from the same gossip history used to disseminate models. Nodes exchange model payloads only with neighbors, while full nodes collect event certificates and receiver-endorsed accepted gossip proofs, reconstruct a compact Topology directed acyclic graph (DAG), and run Hashgraph-style virtual voting followed by compact full-node certificates. Finality is over unique model-origin tuples, not identical local parameter states. To improve resilience, gspDAG-FL combines payload validation, accepted-proof validation, and private semantic audit before aggregation. We formalize the adversarial setting, prove safety and conditional liveness of the control plane, and give a convergence guarantee for certified perturbed gossip under time-varying effective mixing. Experiments on MNIST classification and Penn Treebank language modeling, using fair held-out validation/audit data and networks up to \(N=100\), show that gspDAG-FL achieves learning quality close to validation-based ledger FL while reducing coordination bottlenecks, improving throughput, and maintaining high invalid-origin detection under mixed Byzantine and lazy participation.