Differentially Private Federated Learning via Reconfigurable Intelligent Surface

• URL: http://arxiv.org/abs/2203.17028v1
• Date: Thu, 31 Mar 2022 13:45:02 GMT
• Title: Differentially Private Federated Learning via Reconfigurable Intelligent Surface
• Authors: Yuhan Yang, Yong Zhou, Youlong Wu, Yuanming Shi
• Abstract summary: Federated learning (FL) enables the collaborative training of a global model over decentralized local datasets without sharing them. We propose a reconfigurable intelligent surface (RIS) empowered over-the-air FL system to alleviate the dilemma between learning accuracy and privacy.
• Score: 27.004823731436765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL), as a disruptive machine learning paradigm, enables the collaborative training of a global model over decentralized local datasets without sharing them. It spans a wide scope of applications from Internet-of-Things (IoT) to biomedical engineering and drug discovery. To support low-latency and high-privacy FL over wireless networks, in this paper, we propose a reconfigurable intelligent surface (RIS) empowered over-the-air FL system to alleviate the dilemma between learning accuracy and privacy. This is achieved by simultaneously exploiting the channel propagation reconfigurability with RIS for boosting the receive signal power, as well as waveform superposition property with over-the-air computation (AirComp) for fast model aggregation. By considering a practical scenario where high-dimensional local model updates are transmitted across multiple communication blocks, we characterize the convergence behaviors of the differentially private federated optimization algorithm. We further formulate a system optimization problem to optimize the learning accuracy while satisfying privacy and power constraints via the joint design of transmit power, artificial noise, and phase shifts at RIS, for which a two-step alternating minimization framework is developed. Simulation results validate our systematic, theoretical, and algorithmic achievements and demonstrate that RIS can achieve a better trade-off between privacy and accuracy for over-the-air FL systems.

Related papers

• RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility [35.04722426910211]
  Urban Air Mobility (UAM) expands vehicles from the ground to the near-ground space, envisioned as a revolution in transportation systems. To overcome the challenge, federated learning (FL) and other collaborative learning have been proposed to enable resource-limited devices to conduct onboard deep learning (DL) collaboratively. This paper explores reconfigurable intelligent surfaces (RIS) empowered distributed learning, taking account of topological attributes to facilitate the learning performance with convergence guarantee.
  arXiv  Detail & Related papers  (2024-03-08T08:05:50Z)
• 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)
• Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces [30.1512069754603]
  We propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge. We consider a scenario where new data are continuously generated/collected by a set of devices and are handled through a dynamic queueing system.
  arXiv  Detail & Related papers  (2023-05-18T12:46:42Z)
• Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization [82.12796238714589]
  We propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation. We characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. We establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed.
  arXiv  Detail & Related papers  (2023-05-04T09:26:03Z)
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces [56.35676570414731]
  The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments. RISs offer a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces.
  arXiv  Detail & Related papers  (2022-05-08T06:21:33Z)
• Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods [83.98961686408171]
  Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks. To fully exploit the advantages of RISs in wireless systems, the phases of the reflecting elements must be jointly designed with conventional communication resources. This paper provides a review of current optimization methods and artificial intelligence-based methods for handling the constraints imposed by RIS.
  arXiv  Detail & Related papers  (2022-04-28T09:26:14Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• 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)
• Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces [50.622375361505824]
  Reconfigurable Intelligent Surfaces (RISs) are highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation. One of the major challenges with RIS-empowered wireless communications is the low-overhead dynamic configuration of multiple RISs. We devise low-complexity supervised learning approaches for the RISs' phase configurations.
  arXiv  Detail & Related papers  (2020-10-09T05:35:27Z)

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.