arXiv:2603. 12977v3 Announce Type: replace Abstract: Foundation models are commonly deployed as frozen feature extractors with a small trainable head to adapt to private, user-generated data in federated settings.
By Yijun Quan, Wentai Wu, Giovanni Montana
The paper investigates the feasibility of forget‑only unlearning, where a deletion algorithm can only access the trained model and the examples to forget, without any retained data or additional training information. It demonstrates that the possibility of successful unlearning depends on the learning method, showing that different datasets can yield identical models yet require distinct post‑deletion outputs. The authors establish lower bounds on the accuracy of unlearning relative to retraining and prove that, for certain learners, the algorithm must memorize a substantial portion of the training data—potentially the entire dataset—to handle arbitrary deletion requests, implying that models designed for forget‑only unlearning may need to retain more information than standard training does.
By Luka Radi\'c, Vikrant Singhal, Amartya Sanyal
arXiv:2606. 03808v1 Announce Type: cross Abstract: We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observation: continual learning (CL) and machine unlearning (MU) which are fundamentally dual problems.
By Vedant Jawandhia, Daksh Ahuja, Ghufran Alam Siddiqui, Prashant Trivedi, Yash Sinha, Pratik Narang
The paper introduces Executable Release Certification (ExecCert), a layer that certifies machine‑unlearning artifacts at release time. ExecCert either closes a method’s native certificate for the executed candidate or applies Retraining‑Reference Release Verification (RRV) to ensure fidelity to the current retain‑set. An incremental RRV is developed for frozen representations with a mutable ridge head, allowing certified evidence to be maintained across deletion requests without full reconstruction. Experiments on four unlearning implementations show ExecCert preserves valid certificates, tightens bounds, and improves release decisions, while incremental certification remains cost‑effective in sequential‑service settings.
By Ziyu Zhao, Xinyu Wang, Xiaowen Chang, Yixuan He
The paper introduces Quantized Sufficient Statistics (QSS), a method that separates a frozen global schema from mutable, sum‑decomposable content to enable exact unlearning. QSS offers two variants: QSS‑L, which removes a label while keeping the input, and QSS‑E, which removes both input and label by learning a schema without deletable examples. Experiments on 15 vision, text, and tabular datasets show that QSS‑L achieves deletion latencies up to 483× faster than SISA while maintaining comparable accuracy, and QSS‑E quantifies the accuracy trade‑off when all traces of an input are removed.
By Ami Tavory, Shripad Gade, Tal Sarig, Noam Touitou, Ido Guy
arXiv:2505. 12239v2 Announce Type: replace-cross Abstract: In Continual Learning (CL), using a Pre-Trained Model (PTM) as the feature extractor has become a popular practice.
By Yajiang Huang, Jianheng Tang, Kejia Fan, Huiping Zhuang, Anfeng Liu, Tian Wang, Yunhuai Liu, Mianxiong Dong, Houbing Herbert Song