Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse

• URL: http://arxiv.org/abs/2408.14416v1
• Date: Mon, 26 Aug 2024 17:03:14 GMT
• Title: Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse
• Authors: Yahao Ding, Wen Shang, Minrui Xu, Zhaohui Yang, Ye Hu, Dusit Niyato, Mohammad Shikh-Bahaei,
• Abstract summary: We propose an integrated federated split learning and hyperdimensional computing framework for emerging foundation models. This novel approach reduces communication costs, computation load, and privacy risks, making it suitable for resource-constrained edge devices in the Metaverse.
• Score: 56.384390765357004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Metaverse, a burgeoning collective virtual space merging augmented reality and persistent virtual worlds, necessitates advanced artificial intelligence (AI) and communication technologies to support immersive and interactive experiences. Federated learning (FL) has emerged as a promising technique for collaboratively training AI models while preserving data privacy. However, FL faces challenges such as high communication overhead and substantial computational demands, particularly for neural network (NN) models. To address these issues, we propose an integrated federated split learning and hyperdimensional computing (FSL-HDC) framework for emerging foundation models. This novel approach reduces communication costs, computation load, and privacy risks, making it particularly suitable for resource-constrained edge devices in the Metaverse, ensuring real-time responsive interactions. Additionally, we introduce an optimization algorithm that concurrently optimizes transmission power and bandwidth to minimize the maximum transmission time among all users to the server. The simulation results based on the MNIST dataset indicate that FSL-HDC achieves an accuracy rate of approximately 87.5%, which is slightly lower than that of FL-HDC. However, FSL-HDC exhibits a significantly faster convergence speed, approximately 3.733x that of FSL-NN, and demonstrates robustness to non-IID data distributions. Moreover, our proposed optimization algorithm can reduce the maximum transmission time by up to 64% compared with the baseline.

Related papers

• Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization [7.013344179232109]
  Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data. Current SL algorithms face limitations in training efficiency and suffer from prolonged latency. We propose the Heterogeneous Split Federated Learning framework, which allows resource-constrained clients to train their personalized client-side models in parallel.
  arXiv  Detail & Related papers  (2024-11-21T07:46:01Z)
• Federated Hyperdimensional Computing [14.844383542052169]
  Federated learning (FL) enables a loose set of participating clients to collaboratively learn a global model via coordination by a central server. Existing FL approaches rely on complex algorithms with massive models, such as deep neural networks (DNNs) We first propose FedHDC, a federated learning framework based on hyperdimensional computing (HDC)
  arXiv  Detail & Related papers  (2023-12-26T09:24:19Z)
• Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks [72.59891661768177]
  Federated learning (FL) enables distributed learning across edge devices while protecting data privacy. We consider a FL framework with partial model pruning and personalization to overcome these challenges. This framework splits the learning model into a global part with model pruning shared with all devices to learn data representations and a personalized part to be fine-tuned for a specific device.
  arXiv  Detail & Related papers  (2023-09-04T21:10:45Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy [25.763777765222358]
  Federated learning (FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. As the model size grows, the training latency due to limited transmission bandwidth and private information degrades while using differential privacy (DP) protection. We propose sparsification empowered FL framework wireless channels, in over to improve training efficiency without sacrificing convergence performance.
  arXiv  Detail & Related papers  (2023-04-09T05:21:15Z)
• Over-the-Air Federated Learning via Second-Order Optimization [37.594140209854906]
  Federated learning (FL) could result in task-oriented data traffic flows over wireless networks with limited radio resources. We propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation.
  arXiv  Detail & Related papers  (2022-03-29T12:39:23Z)
• SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks [56.68149211499535]
  Federated learning (FL) is a key enabler for efficient communication and computing leveraging devices' distributed computing capabilities. This paper proposes a novel learning framework by integrating FL and width-adjustable slimmable neural networks (SNNs) We propose a communication and energy-efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models.
  arXiv  Detail & Related papers  (2022-03-26T15:06:13Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks [52.93232352968347]
  This paper aims to integrate two synergetic technologies, federated learning (FL) and width-adjustable slimmable neural network (SNN) FL preserves data privacy by exchanging the locally trained models of mobile devices. SNNs are however non-trivial, particularly under wireless connections with time-varying channel conditions. We propose a communication and energy-efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models.
  arXiv  Detail & Related papers  (2021-12-05T11:17:17Z)
• Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated Learning [4.710427287359642]
  Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. We propose a novel hybrid algorithm, namely genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyper- parameters and genetically modifies the parameters cluster-wise.
  arXiv  Detail & Related papers  (2021-07-15T10:16:05Z)

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.