arXiv Machine Learning September 10, 2026 By Wenrui Yu, Changlong Ji, Johannes Bjerva, Qiongxiu Li

When Topology Betrays Privacy: Lattice-Based Reconstruction Attacks on Secure Aggregation in Decentralized Federated Learning

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The paper demonstrates that in decentralized federated learning, secure aggregation implemented through local neighborhood aggregation can leak private model updates. By exploiting the asymmetric aggregate views available to colluding semi‑honest nodes, the authors formulate the problem as a Hidden Subset Sum Problem and develop a lattice‑based reconstruction attack. Experiments on image, tabular, and text datasets show that attackers can recover honest participants’ updates and reconstruct private training data.

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