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arXiv Machine Learning By Jinsheng Yuan, Yuhang Hao, Weisi Guo, Yun Wu, Chongyan Gu

Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients

Read the original on arXiv Machine Learning →

The paper demonstrates a novel remote Rowhammer attack that targets federated learning (FL) servers by manipulating client sensor observations to induce repetitive clustered memory updates. Using a reinforcement learning attacker, the authors show that they can achieve approximately 70% repeated update rate in FL systems with sparse updates, leading to bit‑flips in the server’s DRAM during tasks such as automatic speech recognition and CIFAR‑10 classification. This work highlights a previously underexplored vulnerability where client‑side adversarial behavior can compromise server memory integrity in FL environments.

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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