A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning
- URL: http://arxiv.org/abs/2405.02320v1
- Date: Sat, 20 Apr 2024 06:27:01 GMT
- Title: A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning
- Authors: Pengcheng Sun, Erwu Liu, Rui Wang,
- Abstract summary: This paper analyze the influence of wireless communication on federated learning (FL) through symbol error rate (SER)
In FL system, non-orthogonal multiple access (NOMA) can be used as the basic communication framework to reduce the communication congestion and interference caused by multiple users.
The gradient parameters are quantized into multiple bits to retain more gradient information to the maximum extent and to improve the tolerance of transmission errors.
- Score: 6.922030110539386
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The quality of wireless communication will directly affect the performance of federated learning (FL), so this paper analyze the influence of wireless communication on FL through symbol error rate (SER). In FL system, non-orthogonal multiple access (NOMA) can be used as the basic communication framework to reduce the communication congestion and interference caused by multiple users, which takes advantage of the superposition characteristics of wireless channels. The Minimum Mean Square Error (MMSE) based serial interference cancellation (SIC) technology is used to recover the gradient of each terminal node one by one at the receiving end. In this paper, the gradient parameters are quantized into multiple bits to retain more gradient information to the maximum extent and to improve the tolerance of transmission errors. On this basis, we designed the SER-based device selection mechanism (SER-DSM) to ensure that the learning performance is not affected by users with bad communication conditions, while accommodating as many users as possible to participate in the learning process, which is inclusive to a certain extent. The experiments show the influence of multi-bit quantization of gradient on FL and the necessity and superiority of the proposed SER-based device selection mechanism.
Related papers
- Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels [11.108614988357008]
We propose an interference-robust semantic communication (IRSC) scheme for general multiple-input multiple-output (MIMO) interference channels.
This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends.
Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches.
arXiv Detail & Related papers (2024-04-10T11:40:22Z) - Communication-Efficient Framework for Distributed Image Semantic
Wireless Transmission [68.69108124451263]
Federated learning-based semantic communication (FLSC) framework for multi-task distributed image transmission with IoT devices.
Each link is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator.
Channel state information-based multiple-input multiple-output transmission module designed to combat channel fading and noise.
arXiv Detail & Related papers (2023-08-07T16:32:14Z) - 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) - CFLIT: Coexisting Federated Learning and Information Transfer [18.30671838758503]
We study the coexistence of over-the-air FL and traditional information transfer (IT) in a mobile edge network.
We propose a coexisting federated learning and information transfer (CFLIT) communication framework, where the FL and IT devices share the wireless spectrum in an OFDM system.
arXiv Detail & Related papers (2022-07-26T13:17:28Z) - Model-based Deep Learning Receiver Design for Rate-Splitting Multiple
Access [65.21117658030235]
This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods.
The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead.
Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
arXiv Detail & Related papers (2022-05-02T12:23:55Z) - Over-the-Air Multi-Task Federated Learning Over MIMO Interference
Channel [17.362158131772127]
We study over-the-air multi-task FL (OA-MTFL) over the multiple-input multiple-output (MIMO) interference channel.
We propose a novel model aggregation method for the alignment of local gradients for different devices.
We show that due to the use of the new model aggregation method, device selection is no longer essential to our scheme.
arXiv Detail & Related papers (2021-12-27T10:42:04Z) - Federated Learning over Wireless IoT Networks with Optimized
Communication and Resources [98.18365881575805]
Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention.
It is of interest to investigate fast responding and accurate FL schemes over wireless systems.
We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
arXiv Detail & Related papers (2021-10-22T13:25:57Z) - Low-Latency Federated Learning over Wireless Channels with Differential
Privacy [142.5983499872664]
In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server.
In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement.
arXiv Detail & Related papers (2021-06-20T13:51:18Z) - Quantized Federated Learning under Transmission Delay and Outage
Constraints [30.892724364965005]
Federated learning is a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge.
In practical systems with limited radio resources, transmission of a large number of model parameters inevitably suffers from quantization errors (QE) and transmission outage (TO)
We propose a robust FL scheme, named FedTOE, which performs joint allocation of wireless resources and quantization bits across the clients to minimize the QE while making the clients have the same TO probability.
arXiv Detail & Related papers (2021-06-17T11:29:12Z) - Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified
Communication-Learning Design Approach [30.1988598440727]
We develop a unified communication-learning optimization problem to jointly optimize device selection, over-the-air transceiver design, and RIS configuration.
Numerical experiments show that the proposed design achieves substantial learning accuracy improvement compared with the state-of-the-art approaches.
arXiv Detail & Related papers (2020-11-20T08:54:13Z) - Communication Efficient Federated Learning with Energy Awareness over
Wireless Networks [51.645564534597625]
In federated learning (FL), the parameter server and the mobile devices share the training parameters over wireless links.
We adopt the idea of SignSGD in which only the signs of the gradients are exchanged.
Two optimization problems are formulated and solved, which optimize the learning performance.
Considering that the data may be distributed across the mobile devices in a highly uneven fashion in FL, a sign-based algorithm is proposed.
arXiv Detail & Related papers (2020-04-15T21:25:13Z)
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.