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

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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