# FiCABU: A Fisher-Based, Context-Adaptive Machine Unlearning Processor for Edge AI

arXiv: https://arxiv.org/abs/2511.05605 · HTML: https://mlsanity.com/paper/2511.05605/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 26 of its 41 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks](https://mlsanity.com/paper/1911.04933/) (2019)
- [Certified Data Removal from Machine Learning Models](https://mlsanity.com/paper/1911.03030/) (2019)
- [Remember What You Want to Forget: Algorithms for Machine Unlearning](https://mlsanity.com/paper/2103.03279/) (2021)
- [Towards Unbounded Machine Unlearning](https://mlsanity.com/paper/2302.09880/) (2023)
- [Model Sparsity Can Simplify Machine Unlearning](https://mlsanity.com/paper/2304.04934/) (2023)
- [Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening](https://mlsanity.com/paper/2308.07707/) (2023)
- [Machine Unlearning of Pre-trained Large Language Models](https://mlsanity.com/paper/2402.15159/) (2024)
- [Machine Unlearning: Solutions and Challenges](https://mlsanity.com/paper/2308.07061/) (2023)
- [On Large Language Model Continual Unlearning](https://mlsanity.com/paper/2407.10223/) (2024)
- [Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models](https://mlsanity.com/paper/2503.06027/) (2025)
- [Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs](https://mlsanity.com/paper/2408.06621/) (2024)
- [Unified Parameter-Efficient Unlearning for LLMs](https://mlsanity.com/paper/2412.00383/) (2024)
- [ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach](https://mlsanity.com/paper/2311.16136/) (2023)
- [Robust Machine Unlearning for Quantized Neural Networks via Adaptive Gradient Reweighting with Similar Labels](https://mlsanity.com/paper/2503.13917/) (2025)
- [Edge Unlearning is Not "on Edge"! An Adaptive Exact Unlearning System on Resource-Constrained Devices](https://mlsanity.com/paper/2410.10128/) (2024)
- [Offline Model Guard: Secure and Private ML on Mobile Devices](https://mlsanity.com/paper/2007.02351/) (2020)
- [DeepClean: Machine Unlearning on the Cheap by Resetting Privacy Sensitive Weights using the Fisher Diagonal](https://mlsanity.com/paper/2311.10448/) (2023)
- [Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones](https://mlsanity.com/paper/2401.15236/) (2024)
- [Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning](https://mlsanity.com/paper/2411.09072/) (2024)

## Abstract (arXiv)

Machine unlearning, driven by privacy regulations and the "right to be forgotten", is increasingly needed at the edge, yet server-centric or retraining-heavy methods are impractical under tight computation and energy budgets. We present FiCABU (Fisher-based Context-Adaptive Balanced Unlearning), a software-hardware co-design that brings unlearning to edge AI processors. FiCABU combines (i) Context-Adaptive Unlearning, which begins edits from back-end layers and halts once the target forgetting is reached, with (ii) Balanced Dampening, which scales dampening strength by depth to preserve retain accuracy. These methods are realized in a full RTL design of a RISC-V edge AI processor that integrates two lightweight IPs for Fisher estimation and dampening into a GEMM-centric streaming pipeline, validated on an FPGA prototype and synthesized in 45 nm for power analysis. Across CIFAR-20 and PinsFaceRecognition with ResNet-18 and ViT, FiCABU achieves random-guess forget accuracy while matching the retraining-free Selective Synaptic Dampening (SSD) baseline on retain accuracy, reducing computation by up to 87.52 percent (ResNet-18) and 71.03 percent (ViT). On the INT8 hardware prototype, FiCABU further improves retain preservation and reduces energy to 6.48 percent (CIFAR-20) and 0.13 percent (PinsFaceRecognition) of the SSD baseline. In sum, FiCABU demonstrates that back-end-first, depth-aware unlearning can be made both practical and efficient for resource-constrained edge AI devices.