Summary of Edge Unlearning Is Not “on Edge”! An Adaptive Exact Unlearning System on Resource-constrained Devices, by Xiaoyu Xia et al.

Edge Unlearning is Not “on Edge”! An Adaptive Exact Unlearning System on Resource-Constrained Devices

by Xiaoyu Xia, Ziqi Wang, Ruoxi Sun, Bowen Liu, Ibrahim Khalil, Minhui Xue

First submitted to arxiv on: 14 Oct 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper presents a novel approach to enabling exact unlearning on resource-constrained devices, such as edge devices, IoT devices, mobile devices, and satellites. The challenge is that existing methods for exact unlearning require significant computational and memory resources, which may not be available on these devices. To address this, the authors propose the Constraint-aware Adaptive Exact Unlearning System at the network Edge (CAUSE), which minimizes retraining overhead by storing sub-models on the device. CAUSE uses a Fibonacci-based replacement strategy to update the number of shards in the user-based data partition process and leverages model pruning to save memory via compression with minimal accuracy sacrifice. The experimental results show that CAUSE outperforms other representative systems in terms of unlearning speed, energy consumption, and accuracy.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Imagine you have a device that can learn from data, but sometimes it’s important to forget some of that data. This is called “unlearning.” The problem is that most methods for unlearning require a lot of resources, like memory and processing power. But what if you’re using a device with limited resources? That’s where this new system comes in. It’s called CAUSE, and it helps devices remember to forget information while still being efficient with their resources.