Over-the-Air Federated Learning via Second-Order Optimization

03/29/2022
by   Peng Yang, et al.
0

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and security guarantees. However, FL could result in task-oriented data traffic flows over wireless networks with limited radio resources. To design communication-efficient FL, most of the existing studies employ the first-order federated optimization approach that has a slow convergence rate. This however results in excessive communication rounds for local model updates between the edge devices and edge server. To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation. This is achieved by exploiting the waveform superposition property of a multi-access channel to implement the distributed second-order optimization algorithm over wireless networks. The convergence behavior of the proposed algorithm is further characterized, which reveals a linear-quadratic convergence rate with an accumulative error term in each iteration. We thus propose a system optimization approach to minimize the accumulated error gap by joint device selection and beamforming design. Numerical results demonstrate the system and communication efficiency compared with the state-of-the-art approaches.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/24/2022

Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning

Federated learning (FL), as an emerging edge artificial intelligence par...
research
11/20/2020

Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach

To exploit massive amounts of data generated at mobile edge networks, fe...
research
09/06/2021

On Second-order Optimization Methods for Federated Learning

We consider federated learning (FL), where the training data is distribu...
research
12/31/2018

Federated Learning via Over-the-Air Computation

The stringent requirements for low-latency and privacy of the emerging h...
research
08/05/2021

Multi-task Federated Edge Learning (MtFEEL) in Wireless Networks

Federated Learning (FL) has evolved as a promising technique to handle d...
research
11/07/2022

How to Coordinate Edge Devices for Over-the-Air Federated Learning?

This work studies the task of device coordination in wireless networks f...
research
05/14/2022

Robust Design of Federated Learning for Edge-Intelligent Networks

Mass data traffics, low-latency wireless services and advanced artificia...

Please sign up or login with your details

Forgot password? Click here to reset