Wireless Quantized Federated Learning: A Joint Computation and Communication Design

2022-03-11 · Pavlos S. Bouzinis, Panagiotis D. Diamantoulakis, George K. Karagiannidis

Recently, federated learning (FL) has sparked widespread attention as a promising decentralized machine learning approach which provides privacy and low delay. However, communication bottleneck still constitutes an issue, that needs to be resolved for an efficient deployment of FL over wireless networks. In this paper, we aim to minimize the total convergence time of FL, by quantizing the local model parameters prior to uplink transmission. More specifically, the convergence analysis of the FL algorithm with stochastic quantization is firstly presented, which reveals the impact of the quantization error on the convergence rate. Following that, we jointly optimize the computing, communication resources and number of quantization bits, in order to guarantee minimized convergence time across all global rounds, subject to energy and quantization error requirements, which stem from the convergence analysis. The impact of the quantization error on the convergence time is evaluated and the trade-off among model accuracy and timely execution is revealed. Moreover, the proposed method is shown to result in faster convergence in comparison with baseline schemes. Finally, useful insights for the selection of the quantization error tolerance are provided.

📄 PDF Abstract BibTeX arXiv:2203.05878

Code (0)

등록된 구현이 없습니다.

Tasks

Federated Learning Quantization

Bayesian Federated Learning over Wireless Networks

2020-12-31 · Seunghoon Lee, Chanho Park, Song-Nam Hong, Yonina C. Eldar 외

Federated learning is a privacy-preserving and distributed training method using heterogeneous data sets stored at local devices. Federated learning over wireless networks requires aggregating locally computed gradients …

Federated Learning Privacy Preserving

Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks

2025-01-20 · Shuai Wang, Yanqing Xu, Chaoqun You, Mingjie Shao 외

Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks, but its practical deployment is hindered by the high communication overhead…

Federated Learning Quantization

Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks

2022-09-21 · Sihua Wang, Mingzhe Chen, Christopher G. Brinton, Changchuan Yin 외

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 o…

Federated Learning Model-based Reinforcement Learning Quantization Reinforcement Learning (RL)

GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks

2026-07-31 · Idan Roth, Lutz Lampe arxiv

Deploying state-of-the-art deep neural networks (DNNs) at the wireless edge is severely bottlenecked by the strict energy and resource constraints of mobile devices. Although federated split learning (FSL) alleviates on-…

Federated Learning

Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning

2024-08-03 · Xinlu Zhang, Yansha Deng, Toktam Mahmoodi

Time-triggered federated learning, in contrast to conventional event-based federated learning, organizes users into tiers based on fixed time intervals. However, this network still faces challenges due to a growing numbe…