Model Sparsification Can Simplify Machine Unlearning

Recent data regulations necessitate machine unlearning (MU): The removal of the effect of specific examples from the model. While exact unlearning is possible by conducting a model retraining with the remaining data from scratch, its computational cost has led to the development of approximate but efficient unlearning schemes. Beyond data-centric MU solutions, we advance MU through a novel model-based viewpoint: sparsification via weight pruning. Our results in both theory and practice indicate that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner, closing the approximation gap, while continuing to be efficient. With this insight, we develop two new sparsity-aware unlearning meta-schemes, termed `prune first, then unlearn' and `sparsity-aware unlearning'. Extensive experiments show that our findings and proposals consistently benefit MU in various scenarios, including class-wise data scrubbing, random data scrubbing, and backdoor data forgetting. One highlight is the 77% unlearning efficacy gain of fine-tuning (one of the simplest approximate unlearning methods) in the proposed sparsity-aware unlearning paradigm. Codes are available at https://github.com/OPTML-Group/Unlearn-Sparse

Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization

The aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of the contribution of a given data point from a training procedure. Federated Unlearning (FU) consists in extending MU to unlearn a given client's contribution from a federated training routine. Current FU approaches are generally not scalable, and do not come with sound theoretical quantification of the effectiveness of unlearning. In this work we present Informed Federated Unlearning (IFU), a novel efficient and quantifiable FU approach. Upon unlearning request from a given client, IFU identifies the optimal FL iteration from which FL has to be reinitialized, with unlearning guarantees obtained through a randomized perturbation mechanism. The theory of IFU is also extended to account for sequential unlearning requests. Experimental results on different tasks and dataset show that IFU leads to more efficient unlearning procedures as compared to basic re-training and state-of-the-art FU approaches

MANAGING OBSOLETE KNOWLEDGE: TOWARDS A CLARIFIED AND CONTEXTUALIZED CONCEPTION OF UNLEARNING

The paper aims at clarifying, specifying, and contextualizing the concept of organizational unlearning in the IS literature, through a systematic analysis of the concept. We suggest a definition of unlearning as an intentional practice in order to reduce the possible negative impacts of obsolete knowledge. Reviewing the IS literature based on the suggested definition, we identify four dominant views of unlearning. Using this definition, we empirically explore how organizations apply unlearning in the case of disruptive IT changes. The insight from the empirical study shows a wide range of unlearning practices which are applied to different organizational and technical factors. In addition, we identify six characteristics of the IS context which have direct bearings on applying unlearning practices. Using these empirical insights, we suggest how the concept of unlearning can be clearly defined and specifically operationalized in order to avoid common misunderstanding of this concept. We conclude by commenting on how the dominant views of unlearning in the IS literature can be completed and enriched

Unlearning before Creating new Knowledge: A Cognitive Process.

Recent research expresses serious doubts on the \ concept of unlearning. It is argued that knowledge \ cannot be discarded or eliminated in order to make \ space for the creation of new knowledge. Taking into \ account the recent scepticism, we focus on the \ cognitive dimension of unlearning and propose an \ alternative conceptualization. Considering how far \ unlearning can go from a psychological/cognitive \ scientific perspective, we propose that unlearning is \ about reducing the influence of old knowledge on our \ cognitive capacity. This study: (a) investigates the \ unlearning process within the cognitive domain and \ on an individual level and (b) proposes unlearning \ process triggers that detract or facilitate the \ knowledge change process, which could subsequently \ contribute to unlearning on an organizational level

Federated Unlearning via Active Forgetting

The increasing concerns regarding the privacy of machine learning models have catalyzed the exploration of machine unlearning, i.e., a process that removes the influence of training data on machine learning models. This concern also arises in the realm of federated learning, prompting researchers to address the federated unlearning problem. However, federated unlearning remains challenging. Existing unlearning methods can be broadly categorized into two approaches, i.e., exact unlearning and approximate unlearning. Firstly, implementing exact unlearning, which typically relies on the partition-aggregation framework, in a distributed manner does not improve time efficiency theoretically. Secondly, existing federated (approximate) unlearning methods suffer from imprecise data influence estimation, significant computational burden, or both. To this end, we propose a novel federated unlearning framework based on incremental learning, which is independent of specific models and federated settings. Our framework differs from existing federated unlearning methods that rely on approximate retraining or data influence estimation. Instead, we leverage new memories to overwrite old ones, imitating the process of \textit{active forgetting} in neurology. Specifically, the model, intended to unlearn, serves as a student model that continuously learns from randomly initiated teacher models. To preserve catastrophic forgetting of non-target data, we utilize elastic weight consolidation to elastically constrain weight change. Extensive experiments on three benchmark datasets demonstrate the efficiency and effectiveness of our proposed method. The result of backdoor attacks demonstrates that our proposed method achieves satisfying completeness

Characteristics of Complete and Incomplete Physicians’ Unlearning with Electronic Medical Record

This study examines the concept of unlearning, the process of disuse or replacement of an action, procedure or belief in favor of a new one, in the context of healthcare. Little is known about the true nature of unlearning and related learning change processes within the context of healthcare. The study of unlearning continues to be important not only due to the nature of the discipline itself, but physicians are required to support knowledge change for improved care quality. The study argues the introduction of new Health Information Technologies (HITs), such as EMRs, affect the unlearning process in physician providers. We address the following research question: “What are the characteristics of the unlearning process by physicians who are using EMRs?” using a qualitative case study methodology. Interviews, the primary data collection method and coding is mainly used for data analysis. Results show physician unlearning is characterized as either complete unlearning or incomplete unlearning

Are There Typological Characteristics of Individual Unlearning?

Organizations have sought solutions to produce consistent, competent practices while updating organizational processes. A traditional method of learning used strategies of identifying gaps in knowledge, and teaching lacking information to close gaps. Faulty learning completion processes often yield decreased work product quality, and productivity, or increased product costs. Knowledge base change creates ongoing difficulties for individuals who must unlearn, store, and use new knowledge processes to update the old. Knowledge change, or unlearning, speculated to involve a replacement of prior knowledge remains unconceptualized due to limited, anecdotally based research. This qualitative study aims to further characterize unlearning initiation processes, and clarify knowledge replacement factors: 1) How does individual unlearning initiate? and, 2) What factors contribute to the unlearning process? Three weekly-spaced interviews with 31 participants categorized unlearning using Rushmer and Davies’ (2004) typological unlearning model. Predominately two knowledge change typologies were demonstrated and a new unlearning model developed

On Learning and Unlearning

I remember passing our lunch lady–the nice one with a big bleach-blond afro. She was perched on an elementary-school-sized desk, eyes fixated to the television. I glanced at the screen on the way into my classroom while my teacher hesitated in the hallway, whispering to the other adults. She reentered the room a few minutes later to explain. In the following months, my television provided me with one of the most formative, practical and comprehensive educational experiences of my life. First it was vocabulary building, with the words like “hi-jacker,” and “terrorist.” Then it was physics, learning that inertia is the reason for absolute devastation when your plane crashes into a building. Soon, “Al-Qaeda,” “the Taliban,” and “Osama bin Laden” became part of my reality, as I watched a broadcast of young men in the “Middle East” (I was learning geography too!) burning American flags. [excerpt

From Adaptive Query Release to Machine Unlearning

We formalize the problem of machine unlearning as design of efficient unlearning algorithms corresponding to learning algorithms which perform a selection of adaptive queries from structured query classes. We give efficient unlearning algorithms for linear and prefix-sum query classes. As applications, we show that unlearning in many problems, in particular, stochastic convex optimization (SCO), can be reduced to the above, yielding improved guarantees for the problem. In particular, for smooth Lipschitz losses and any $\rho>0$, our results yield an unlearning algorithm with excess population risk of $\tilde O\big(\frac{1}{\sqrt{n}}+\frac{\sqrt{d}}{n\rho}\big)$ with unlearning query (gradient) complexity $\tilde O(\rho \cdot \text{Retraining Complexity})$, where $d$ is the model dimensionality and $n$ is the initial number of samples. For non-smooth Lipschitz losses, we give an unlearning algorithm with excess population risk $\tilde O\big(\frac{1}{\sqrt{n}}+\big(\frac{\sqrt{d}}{n\rho}\big)^{1/2}\big)$ with the same unlearning query (gradient) complexity. Furthermore, in the special case of Generalized Linear Models (GLMs), such as those in linear and logistic regression, we get dimension-independent rates of $\tilde O\big(\frac{1}{\sqrt{n}} +\frac{1}{(n\rho)^{2/3}}\big)$ and $\tilde O\big(\frac{1}{\sqrt{n}} +\frac{1}{(n\rho)^{1/3}}\big)$ for smooth Lipschitz and non-smooth Lipschitz losses respectively. Finally, we give generalizations of the above from one unlearning request to \textit{dynamic} streams consisting of insertions and deletions.Comment: Accepted to ICML 202

Unlearning Nonlinear Graph Classifiers in the Limited Training Data Regime

As the demand for user privacy grows, controlled data removal (machine unlearning) is becoming an important feature of machine learning models for data-sensitive Web applications such as social networks and recommender systems. Nevertheless, at this point it is still largely unknown how to perform efficient machine unlearning of graph neural networks (GNNs); this is especially the case when the number of training samples is small, in which case unlearning can seriously compromise the performance of the model. To address this issue, we initiate the study of unlearning the Graph Scattering Transform (GST), a mathematical framework that is efficient, provably stable under feature or graph topology perturbations, and offers graph classification performance comparable to that of GNNs. Our main contribution is the first known nonlinear approximate graph unlearning method based on GSTs. Our second contribution is a theoretical analysis of the computational complexity of the proposed unlearning mechanism, which is hard to replicate for deep neural networks. Our third contribution are extensive simulation results which show that, compared to complete retraining of GNNs after each removal request, the new GST-based approach offers, on average, a $10.38$x speed-up and leads to a $2.6$% increase in test accuracy during unlearning of $90$ out of $100$ training graphs from the IMDB dataset ($10$% training ratio)