arXiv (All)
open access
Phantom Forgetting: How BatchNorm State Fakes Deletion in Edge Split Machine Unlearning
Split learning is a natural fit for edge intelligence: a resource-constrained edge device runs the first layers of a network and offloads the remainder to a server, so that raw data never leave the device. When such a system must unlearn revoked or sensitive data for example to honor a right-to-eras
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Phantom Forgetting: How BatchNorm State Fakes Deletion in Edge Split Machine Unlearning#1450683
- Memory-Coupled Unlearning for Data-Absent Identity Forgetting#1324749
- Machine unlearning using forgetting neural networks#405948
- When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks#1343656
- Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure#31240
Record · ID 1457954
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive