Sample-Driven Federated Learning for Energy-Efficient and Real-Time IoT Sensing

• URL: http://arxiv.org/abs/2310.07497v1
• Date: Wed, 11 Oct 2023 13:50:28 GMT
• Title: Sample-Driven Federated Learning for Energy-Efficient and Real-Time IoT Sensing
• Authors: Minh Ngoc Luu, Minh-Duong Nguyen, Ebrahim Bedeer, Van Duc Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Quoc-Viet Pham
• Abstract summary: We introduce an online reinforcement learning algorithm named Sample-driven Control for Federated Learning (SCFL) built on the Soft Actor-Critic (A2C) framework. SCFL enables the agent to dynamically adapt and find the global optima even in changing environments.
• Score: 22.968661040226756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the domain of Federated Learning (FL) systems, recent cutting-edge methods heavily rely on ideal conditions convergence analysis. Specifically, these approaches assume that the training datasets on IoT devices possess similar attributes to the global data distribution. However, this approach fails to capture the full spectrum of data characteristics in real-time sensing FL systems. In order to overcome this limitation, we suggest a new approach system specifically designed for IoT networks with real-time sensing capabilities. Our approach takes into account the generalization gap due to the user's data sampling process. By effectively controlling this sampling process, we can mitigate the overfitting issue and improve overall accuracy. In particular, We first formulate an optimization problem that harnesses the sampling process to concurrently reduce overfitting while maximizing accuracy. In pursuit of this objective, our surrogate optimization problem is adept at handling energy efficiency while optimizing the accuracy with high generalization. To solve the optimization problem with high complexity, we introduce an online reinforcement learning algorithm, named Sample-driven Control for Federated Learning (SCFL) built on the Soft Actor-Critic (A2C) framework. This enables the agent to dynamically adapt and find the global optima even in changing environments. By leveraging the capabilities of SCFL, our system offers a promising solution for resource allocation in FL systems with real-time sensing capabilities.

Related papers

• Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanism [0.48342038441006796]
  Federated Learning (FL) is a decentralized learning approach that protects sensitive information by utilizing local model parameters rather than sharing clients' raw datasets. This paper investigates the deployment and integration of two lightweight Hyper- Optimization (HPO) tools, Raytune and Optuna, within the context of FL settings. To this end, both local and global feedback mechanisms are integrated to limit the search space and expedite the HPO process.
  arXiv  Detail & Related papers  (2024-11-19T05:49:00Z)
• Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
  In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions. This paper proposes a digital twin (DT) and federated digital twin (FL) scheme. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
  arXiv  Detail & Related papers  (2024-11-04T17:48:02Z)
• Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices [72.61177465035031]
  We propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy.
  arXiv  Detail & Related papers  (2023-10-21T12:07:04Z)
• Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework [70.83511997272457]
  We propose a semi-federated learning (SemiFL) paradigm to leverage both the base station (BS) and devices for a hybrid implementation of centralized learning (CL) and FL. We propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers.
  arXiv  Detail & Related papers  (2023-10-04T03:32:39Z)
• 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)
• AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices [20.52519915112099]
  We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates. Experiment results indicate that, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy.
  arXiv  Detail & Related papers  (2023-01-08T15:25:55Z)
• Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks [103.22651843174471]
  This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which aggregates them into a quantized global model and synchronizes the devices. We show that the FL training process can be described as a Markov decision process and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations.
  arXiv  Detail & Related papers  (2022-09-21T08:52:51Z)
• Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System [0.2609784101826761]
  Federated Learning (FL) decouples model training from the need for direct access to the data. In this work, we investigated the impact of different hyperparameter optimization approaches in an FL system. We implemented these approaches based on grid search and Bayesian optimization and evaluated the algorithms on the MNIST data set and on the Internet of Things (IoT) sensor based industrial data set.
  arXiv  Detail & Related papers  (2021-10-15T17:01:40Z)
• Cost-Effective Federated Learning in Mobile Edge Networks [37.16466118235272]
  Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model without sharing their raw data. We analyze how to design adaptive FL in mobile edge networks that optimally chooses essential control variables to minimize the total cost. We develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters.
  arXiv  Detail & Related papers  (2021-09-12T03:02:24Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems [40.421253127588244]
  Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. We first propose an efficient FL algorithm (called FedFog) to perform the local aggregation of gradient parameters at fog servers and global training update at the cloud.
  arXiv  Detail & Related papers  (2021-07-04T08:03:15Z)

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.