Client-Side Probing of Deleted Ridge Statistics in Federated Unlearning
Federated unlearning aims to remove a client's data from a shared model without retraining from scratch. Some efficient systems make deletion exact by storing compact, additive summaries of the training features and broadcasting an updated linear classifier after every accepted change. We show that these broadcasts can also reveal the hidden summaries. A malicious client can submit known changes, use the returned classifiers to identify the server state, and compare states immediately before and after an isolated deletion. This exposes the deleted sample, class, or client summary and can enable its reinsertion. We characterize exactly when the observations contain enough independent infor...
| Authors | Yijun Quan, Giovanni Montana |
|---|---|
| Primary category | cs.CR |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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