SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications

• URL: http://arxiv.org/abs/2502.19785v1
• Date: Thu, 27 Feb 2025 05:49:04 GMT
• Title: SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications
• Authors: Weiqi Wang, Zhiyi Tian, Chenhan Zhang, Shui Yu,
• Abstract summary: Most semantic training datasets contain personal private information.<n>Existing machine unlearning solutions remove data contribution from trained models.<n>We propose a semantic communication unlearning scheme to tackle the problem.
• Score: 14.235752586133158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning (DL) enabled semantic communications leverage DL to train encoders and decoders (codecs) to extract and recover semantic information. However, most semantic training datasets contain personal private information. Such concerns call for enormous requirements for specified data erasure from semantic codecs when previous users hope to move their data from the semantic system. {Existing machine unlearning solutions remove data contribution from trained models, yet usually in supervised sole model scenarios. These methods are infeasible in semantic communications that often need to jointly train unsupervised encoders and decoders.} In this paper, we investigate the unlearning problem in DL-enabled semantic communications and propose a semantic communication unlearning (SCU) scheme to tackle the problem. {SCU includes two key components. Firstly,} we customize the joint unlearning method for semantic codecs, including the encoder and decoder, by minimizing mutual information between the learned semantic representation and the erased samples. {Secondly,} to compensate for semantic model utility degradation caused by unlearning, we propose a contrastive compensation method, which considers the erased data as the negative samples and the remaining data as the positive samples to retrain the unlearned semantic models contrastively. Theoretical analysis and extensive experimental results on three representative datasets demonstrate the effectiveness and efficiency of our proposed methods.

Related papers

• CodeUnlearn: Amortized Zero-Shot Machine Unlearning in Language Models Using Discrete Concept [5.345828824625758]
  We propose a novel amortized unlearning approach using codebook features and Sparse Autoencoders (SAEs) By leveraging a bottleneck to decompose the activation space and regulate information flow, our method efficiently unlearns targeted information while preserving the model's performance on unrelated data.
  arXiv  Detail & Related papers  (2024-10-08T10:26:22Z)
• Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification [50.28867343337997]
  This work presents a TinyML-based semantic communication framework for few-shot wireless image classification. We exploit split-learning to limit the computations performed by the end-users while ensuring privacy-preserving. meta-learning overcomes data availability concerns and speeds up training by utilizing similarly trained tasks.
  arXiv  Detail & Related papers  (2024-09-03T05:56:55Z)
• Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models [52.03511469562013]
  We introduce the Iterative Contrastive Unlearning (ICU) framework, which consists of three core components.<n>A Knowledge Unlearning Induction module targets specific knowledge for removal using an unlearning loss.<n>A Contrastive Learning Enhancement module preserves the model's expressive capabilities against the pure unlearning goal.<n>An Iterative Unlearning Refinement module dynamically adjusts the unlearning process through ongoing evaluation and updates.
  arXiv  Detail & Related papers  (2024-07-25T07:09:35Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)
• Knowledge Distillation Based Semantic Communications For Multiple Users [10.770552656390038]
  We consider the semantic communication (SemCom) system with multiple users, where there is a limited number of training samples and unexpected interference. We propose a knowledge distillation (KD) based system where Transformer based encoder-decoder is implemented as the semantic encoder-decoder and fully connected neural networks are implemented as the channel encoder-decoder. Numerical results demonstrate that KD significantly improves the robustness and the generalization ability when applied to unexpected interference, and it reduces the performance loss when compressing the model size.
  arXiv  Detail & Related papers  (2023-11-23T03:28:14Z)
• Unlearn What You Want to Forget: Efficient Unlearning for LLMs [92.51670143929056]
  Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data. This process might suffer from privacy issues and violations of data protection regulations. We propose an efficient unlearning framework that could efficiently update LLMs without having to retrain the whole model after data removals.
  arXiv  Detail & Related papers  (2023-10-31T03:35:59Z)
• Semantics Alignment via Split Learning for Resilient Multi-User Semantic Communication [56.54422521327698]
  Recent studies on semantic communication rely on neural network (NN) based transceivers such as deep joint source and channel coding (DeepJSCC) Unlike traditional transceivers, these neural transceivers are trainable using actual source data and channels, enabling them to extract and communicate semantics. We propose a distributed learning based solution, which leverages split learning (SL) and partial NN fine-tuning techniques.
  arXiv  Detail & Related papers  (2023-10-13T20:29:55Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme [17.4244108919728]
  To serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic communications. This paper proposes a deep separate source-channel coding framework for the joint task and data oriented semantic communications. An iterative training algorithm is proposed to tackle the overfitting issue of deep learning models.
  arXiv  Detail & Related papers  (2023-02-27T08:34:37Z)
• Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding [11.101344530143303]
  We propose an online learned joint source and channel coding approach that leverages the deep learning model's overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the model or representations to an individual data or channel state instance.
  arXiv  Detail & Related papers  (2022-11-08T16:00:27Z)
• Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data [43.308832291174106]
  This paper proposes a new neural network-based semantic communication system for image transmission. The proposed method can be adaptive to observable datasets while keeping high performance in terms of both data recovery and task execution.
  arXiv  Detail & Related papers  (2022-04-30T13:45:50Z)
• Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data [69.30452751012568]
  We develop a learnable feature generator to diversify exemplars by adaptively generating diverse counterparts of exemplars. We introduce semantic contrastive learning to enforce the generated samples to be semantic consistent with exemplars. Our method does not bring any extra inference cost and outperforms state-of-the-art methods on two benchmarks.
  arXiv  Detail & Related papers  (2022-04-19T15:15:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.