Blind Federated Edge Learning

• URL: http://arxiv.org/abs/2010.10030v1
• Date: Mon, 19 Oct 2020 16:22:28 GMT
• Title: Blind Federated Edge Learning
• Authors: Mohammad Mohammadi Amiri, Tolga M. Duman, Deniz Gunduz, Sanjeev R. Kulkarni, H. Vincent Poor
• Abstract summary: We study federated edge learning (FEEL), where wireless edge devices, each with its own dataset, learn a global model. We propose an analog over-the-air' aggregation scheme, in which the devices transmit their local updates in an uncoded fashion.
• Score: 93.29571175702735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study federated edge learning (FEEL), where wireless edge devices, each with its own dataset, learn a global model collaboratively with the help of a wireless access point acting as the parameter server (PS). At each iteration, wireless devices perform local updates using their local data and the most recent global model received from the PS, and send their local updates to the PS over a wireless fading multiple access channel (MAC). The PS then updates the global model according to the signal received over the wireless MAC, and shares it with the devices. Motivated by the additive nature of the wireless MAC, we propose an analog `over-the-air' aggregation scheme, in which the devices transmit their local updates in an uncoded fashion. Unlike recent literature on over-the-air edge learning, here we assume that the devices do not have channel state information (CSI), while the PS has imperfect CSI. Instead, the PS is equipped multiple antennas to alleviate the destructive effect of the channel, exacerbated due to the lack of perfect CSI. We design a receive beamforming scheme at the PS, and show that it can compensate for the lack of perfect CSI when the PS has a sufficient number of antennas. We also derive the convergence rate of the proposed algorithm highlighting the impact of the lack of perfect CSI, as well as the number of PS antennas. Both the experimental results and the convergence analysis illustrate the performance improvement of the proposed algorithm with the number of PS antennas, where the wireless fading MAC becomes deterministic despite the lack of perfect CSI when the PS has a sufficiently large number of antennas.

Related papers

• Communication-Efficient Federated Learning over Wireless Channels via Gradient Sketching [23.523969065599193]
  We propose Federated Proximal Sketching (FPS), tailored towards band-limited wireless channels. FPS uses a count sketch data structure to address the bandwidth bottleneck and enable efficient compression. We demonstrate the stability, accuracy, and efficiency of FPS in comparison to state-of-the-art methods on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-10-30T20:01:08Z)
• Blind Federated Learning via Over-the-Air q-QAM [11.956183457374186]
  We investigate edge learning over a fading multiple federated channel. We introduce a pioneering digital-the-air modulation over the federated uplink-the-air channel. We find that the number of antennas at the edge server and adopting higher-order modulations improve the accuracy up to 60%.
  arXiv  Detail & Related papers  (2023-11-07T12:02:59Z)
• Semantic Segmentation of Radar Detections using Convolutions on Point Clouds [59.45414406974091]
  We introduce a deep-learning based method to convolve radar detections into point clouds. We adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.
  arXiv  Detail & Related papers  (2023-05-22T07:09:35Z)
• Federated Deep Reinforcement Learning for THz-Beam Search with Limited CSI [17.602598143822913]
  Terahertz (THz) communication with ultra-wide available spectrum is a promising technique that can achieve the stringent requirement of high data rate in the next-generation wireless networks. Finding beam directions for a large-scale antenna array to effectively overcome severe propagation attenuation of THz signals is a pressing need. This paper proposes a novel approach of federated deep reinforcement learning (FDRL) to swiftly perform THz-beam search for multiple base stations.
  arXiv  Detail & Related papers  (2023-04-25T19:28:15Z)
• Digital Over-the-Air Federated Learning in Multi-Antenna Systems [30.137208705209627]
  We study the performance optimization of federated learning (FL) over a realistic wireless communication system with digital modulation and over-the-air computation (AirComp) We propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. An artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission.
  arXiv  Detail & Related papers  (2023-02-04T07:26:06Z)
• Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning [60.94661435297309]
  The scale of mobile networks makes it challenging to optimize antenna parameters using manual intervention or hand-engineered strategies. We propose a new multi-agent reinforcement learning algorithm to optimize mobile network configurations globally. We empirically demonstrate the performance of the algorithm on an antenna tilt tuning problem and a joint tilt and power control problem in a simulated environment.
  arXiv  Detail & Related papers  (2023-01-20T17:06:34Z)
• GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi [52.530330427538885]
  WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring. We propose an end-to-end Gabor residual anti-aliasing sensing network (GraSens) to directly recognize the actions using the WiFi signals from the wireless devices in diverse scenarios.
  arXiv  Detail & Related papers  (2022-05-24T10:20:16Z)
• Accelerated Gradient Descent Learning over Multiple Access Fading Channels [9.840290491547162]
  We consider a distributed learning problem in a wireless network, consisting of N distributed edge devices and a parameter server (PS) We develop a novel Accelerated Gradient-descent Multiple Access (AGMA) algorithm that uses momentum-based gradient signals over noisy fading MAC to improve the convergence rate as compared to existing methods.
  arXiv  Detail & Related papers  (2021-07-26T19:51:40Z)
• Convergence of Federated Learning over a Noisy Downlink [84.55126371346452]
  We study federated learning, where power-limited wireless devices utilize their local datasets to collaboratively train a global model with the help of a remote parameter server. This framework requires downlink transmission from the PS to the devices and uplink transmission from the devices to the PS. The goal of this study is to investigate the impact of the bandwidth-limited shared wireless medium in both the downlink and uplink on the performance of FL.
  arXiv  Detail & Related papers  (2020-08-25T16:15:05Z)
• 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.