TinyML NLP Approach for Semantic Wireless Sentiment Classification

• URL: http://arxiv.org/abs/2411.06291v1
• Date: Sat, 09 Nov 2024 21:26:59 GMT
• Title: TinyML NLP Approach for Semantic Wireless Sentiment Classification
• Authors: Ahmed Y. Radwan, Mohammad Shehab, Mohamed-Slim Alouini,
• Abstract summary: We introduce split learning (SL) as an energy-efficient alternative, privacy-preserving tiny machine learning (MLTiny) scheme. Our results show that SL reduces processing power and CO2 emissions while maintaining high accuracy, whereas FL offers a balanced compromise between efficiency and privacy.
• Score: 49.801175302937246
• License:
• Abstract: Natural Language Processing (NLP) operations, such as semantic sentiment analysis and text synthesis, may often impair users' privacy and demand significant on device computational resources. Centralized learning (CL) on the edge offers an alternative energy-efficient approach, yet requires the collection of raw information, which affects the user's privacy. While Federated learning (FL) preserves privacy, it requires high computational energy on board tiny user devices. We introduce split learning (SL) as an energy-efficient alternative, privacy-preserving tiny machine learning (TinyML) scheme and compare it to FL and CL in the presence of Rayleigh fading and additive noise. Our results show that SL reduces processing power and CO2 emissions while maintaining high accuracy, whereas FL offers a balanced compromise between efficiency and privacy. Hence, this study provides insights into deploying energy-efficient, privacy-preserving NLP models on edge devices.

Related papers

• CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness [6.881974834597426]
  Federated learning (FL) has emerged as a promising framework for distributed machine learning. We introduce CorBin-FL, a privacy mechanism that uses correlated binary quantization to achieve differential privacy. We also propose AugCorBin-FL, an extension that, in addition to PLDP, user-level and sample-level central differential privacy guarantees.
  arXiv  Detail & Related papers  (2024-09-20T00:23:44Z)
• DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV [27.713716107122572]
  In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significant manual effort. We address energy consumption and delay in the BFSSL process by proposing a Deep Reinforcement Learning (DRL)-based resource allocation scheme.
  arXiv  Detail & Related papers  (2024-08-17T13:12:04Z)
• Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning [51.02352381270177]
  Split Federated Learning (SFL) has recently emerged as a promising distributed learning technology. The choice of the cut layer in SFL can have a substantial impact on the energy consumption of clients and their privacy. This article provides a comprehensive overview of the SFL process and thoroughly analyze energy consumption and privacy.
  arXiv  Detail & Related papers  (2023-11-15T23:23:42Z)
• User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks [20.09415156099031]
  Hierarchical FL (HFL) can reduce energy consumption and latency through effective resource allocation and appropriate user assignment. This article proposes a spectrum resource optimization algorithm (SROA) and a two-stage CPU algorithm (TSIA) for HFL. Experimental results demonstrate the superiority of the proposed HFL framework over existing studies in energy and latency reduction.
  arXiv  Detail & Related papers  (2023-09-17T12:10:39Z)
• Binary Federated Learning with Client-Level Differential Privacy [7.854806519515342]
  Federated learning (FL) is a privacy-preserving collaborative learning framework. Existing FL systems typically adopt Federated Average (FedAvg) as the training algorithm. We propose a communication-efficient FL training algorithm with differential privacy guarantee.
  arXiv  Detail & Related papers  (2023-08-07T06:07:04Z)
• A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks [53.561797148529664]
  Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner. Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified. The current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization.
  arXiv  Detail & Related papers  (2023-06-25T13:10:38Z)
• Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning [86.08285033925597]
  This paper presents a new DP perturbation mechanism with a time-varying noise amplitude to protect the privacy of federated learning. We derive an online refinement of the series to prevent FL from premature convergence resulting from excessive perturbation noise. The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.
  arXiv  Detail & Related papers  (2023-03-07T22:52:40Z)
• Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning [74.53077322713548]
  We investigate the privacy-preserving joint edge association and power allocation problem. The proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.
  arXiv  Detail & Related papers  (2023-01-26T10:09:23Z)
• Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations [57.20885629270732]
  We consider privacy aspects of wireless federated learning with Over-the-Air (OtA) transmission of gradient updates from multiple users/agents to an edge server. Traditional perturbation-based methods provide privacy protection while sacrificing the training accuracy. In this work, we aim at minimizing privacy leakage to the adversary and the degradation of model accuracy at the edge server.
  arXiv  Detail & Related papers  (2022-10-05T13:13:35Z)
• Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning [4.25234252803357]
  Federated edge learning (FEEL) is a promising distributed learning technique for next-generation wireless networks. FEEL might significantly shorten energy-constrained participating devices' lifetime due to the power consumed during the model training round. This paper proposes a novel approach that endeavors to minimize computation and communication energy consumption during FEEL rounds.
  arXiv  Detail & Related papers  (2021-03-30T13:34:40Z)

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.