Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach

• URL: http://arxiv.org/abs/2102.07972v1
• Date: Tue, 16 Feb 2021 06:21:08 GMT
• Title: Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach
• Authors: Junshan Zhang, Na Li, Mehmet Dedeoglu
• Abstract summary: We consider a many-to-one wireless architecture for federated learning at the network edge. The local updates at edge devices should be carefully crafted and compressed to match the wireless communication resources available. We propose SGD-based bandlimited coordinate descent algorithms for such settings.
• Score: 28.616890702473526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider a many-to-one wireless architecture for federated learning at the network edge, where multiple edge devices collaboratively train a model using local data. The unreliable nature of wireless connectivity, together with constraints in computing resources at edge devices, dictates that the local updates at edge devices should be carefully crafted and compressed to match the wireless communication resources available and should work in concert with the receiver. Thus motivated, we propose SGD-based bandlimited coordinate descent algorithms for such settings. Specifically, for the wireless edge employing over-the-air computing, a common subset of k-coordinates of the gradient updates across edge devices are selected by the receiver in each iteration, and then transmitted simultaneously over k sub-carriers, each experiencing time-varying channel conditions. We characterize the impact of communication error and compression, in terms of the resulting gradient bias and mean squared error, on the convergence of the proposed algorithms. We then study learning-driven communication error minimization via joint optimization of power allocation and learning rates. Our findings reveal that optimal power allocation across different sub-carriers should take into account both the gradient values and channel conditions, thus generalizing the widely used water-filling policy. We also develop sub-optimal distributed solutions amenable to implementation.

Related papers

• Joint Sensing, Communication, and Computation for Vertical Federated Edge Learning in Edge Perception Network [75.78245138352698]
  In this paper, we consider an integrated sensing, communication, and computation-enabled edge perception network.<n>Multiple edge devices utilize wireless signals to sense environmental information for updating their local models, and the edge server aggregates feature embeddings via over-the-air computation for global model training.<n>First, we analyze the convergence behavior of the ISCC-enabled VFEEL in terms of the loss function degradation in the presence of wireless sensing noise and aggregation distortions during AirComp.
  arXiv  Detail & Related papers  (2025-12-03T02:20:58Z)
• Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning [52.904670248426626]
  This paper studies an over-the-air federated edge learning (Air-FEEL) system with integrated sensing, communication, and computation.<n>We derive a low-complexity I SCC algorithm by alternately optimizing the batch size control and the network resource allocation.
  arXiv  Detail & Related papers  (2025-08-21T02:46:46Z)
• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
  Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks. Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance. We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
  arXiv  Detail & Related papers  (2025-01-20T04:26:21Z)
• Collaborative Edge AI Inference over Cloud-RAN [37.3710464868215]
  A cloud radio access network (Cloud-RAN) based collaborative edge AI inference architecture is proposed. Specifically, geographically distributed devices capture real-time noise-corrupted sensory data samples and extract the noisy local feature vectors. We allow each RRH receives local feature vectors from all devices over the same resource blocks simultaneously by leveraging an over-the-air computation (AirComp) technique. These aggregated feature vectors are quantized and transmitted to a central processor for further aggregation and downstream inference tasks.
  arXiv  Detail & Related papers  (2024-04-09T04:26:16Z)
• Over-the-Air Federated Learning and Optimization [52.5188988624998]
  We focus on Federated learning (FL) via edge-the-air computation (AirComp) We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings. For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error. In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
  arXiv  Detail & Related papers  (2023-10-16T05:49:28Z)
• Adaptive Federated Pruning in Hierarchical Wireless Networks [69.6417645730093]
  Federated Learning (FL) is a privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. In this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale. We show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50 percent communication cost.
  arXiv  Detail & Related papers  (2023-05-15T22:04:49Z)
• Scalable Hierarchical Over-the-Air Federated Learning [3.8798345704175534]
  This work introduces a new two-level learning method designed to handle both interference and device data heterogeneity. We present a comprehensive mathematical approach to derive the convergence bound for the proposed algorithm. Despite the interference and data heterogeneity, the proposed algorithm achieves high learning accuracy for a variety of parameters.
  arXiv  Detail & Related papers  (2022-11-29T12:46:37Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Learning Resilient Radio Resource Management Policies with Graph Neural Networks [124.89036526192268]
  We formulate a resilient radio resource management problem with per-user minimum-capacity constraints. We show that we can parameterize the user selection and power control policies using a finite set of parameters. Thanks to such adaptation, our proposed method achieves a superior tradeoff between the average rate and the 5th percentile rate.
  arXiv  Detail & Related papers  (2022-03-07T19:40:39Z)
• Learning Optimal Antenna Tilt Control Policies: A Contextual Linear Bandit Approach [65.27783264330711]
  Controlling antenna tilts in cellular networks is imperative to reach an efficient trade-off between network coverage and capacity. We devise algorithms learning optimal tilt control policies from existing data. We show that they can produce optimal tilt update policy using much fewer data samples than naive or existing rule-based learning algorithms.
  arXiv  Detail & Related papers  (2022-01-06T18:24:30Z)
• Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks [35.80470886180477]
  We propose a federated learning algorithm that is suitable for cellular wireless networks. We prove its convergence, and provide the optimal scheduling policy that maximizes the convergence rate.
  arXiv  Detail & Related papers  (2020-12-09T16:16:43Z)
• Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks [0.0]
  In our setup, the available limited bandwidth resources are allocated in order to serve randomly arriving service demands. We propose a distributional Deep Deterministic Policy Gradient (DDPG) algorithm combined with Deep Sets to tackle the problem. Our proposed algorithm is tested on both synthetic and real data, showing consistent gains against state-of-the-art conventional methods.
  arXiv  Detail & Related papers  (2020-11-27T09:49:38Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)

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.