Federated Learning via Over-the-Air Computation With Statistical Channel State Information

Jing, Shusen; Xiao, Chengshan

doi:10.1109/twc.2022.3175887

Cited by 24 publications

(7 citation statements)

References 18 publications

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“…Given the privacy guarantees that prevent direct access to training pipelines and clients' data for anomaly detection, the aggregator serves as a crucial defense against potential attacks. It is accountable for incorporating suitable mechanisms to discard and identify abnormal client updates [49]. The adaptive federated optimization concept was presented by Google's research team [50] to increase the flexibility of server optimization.…”

Section: Aggregation Techniquesmentioning

confidence: 99%

Design Pattern and Challenges of Federated Learning with Applications in Industrial Control System

Batool,

Xu,

Rehman

et al. 2024

JAI

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Federated Learning (FL) appeared as an encouraging approach for handling decentralized data. Creating a FL system needs both machine learning (ML) knowledge and thinking about how to design system software.Researchers have focused a lot on the ML side of FL, but have not paid enough attention to designing the software architecture. So, in this survey, a set of design patterns is described to tackle the design issues. Design patterns are like reusable solutions for common problems that come up when designing software architecture. This paper focuses on (1) design patterns such as architectures, frameworks, client selection protocols, personalization techniques, and model aggregation techniques that are building blocks of the FL system. It inquires about tradeoffs and working principles accompanying each design aspect, providing insights into their effect on the scalability, performance, or security process; (2) elaborates challenges faced in the design and execution of FL systems such as communication efficiency, statistical/system heterogeneity, or security/privacy concerns. It additionally investigates continuous exploration efforts and distinguishes future examination headings to take out the design challenges and upgrade the adequacy of the frameworks, and (3) depicts some FL applications used in industrial control systems along with their limitations that pave a new research gap for industry professionals. This comprehensive study provides a valuable resource for researchers, practitioners, and system designers interested in understanding the design aspects and challenges associated with FL.

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Section: Aggregation Techniquesmentioning

confidence: 99%

Design Pattern and Challenges of Federated Learning with Applications in Industrial Control System

Batool,

Xu,

Rehman

et al. 2024

JAI

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“…As for the model aggregation problem, Fan et al [37] first characterize the intrinsic temporal structure of the model aggregation series via a Markovian probability model and develop a message passing based algorithm with low complexity and near-optimal performance. For further communication efficiency, Jing et al [64] adopt statistical channel state information. However, traditional centralized FL still exists other challenges including statistical heterogeneity, computation overhead, security and privacy [140].…”

Section: Centralized Flmentioning

confidence: 99%

Advancements in Federated Learning: Models, Methods, and Privacy

Chen¹,

Wang²,

Jin³

et al. 2023

Preprint

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Federated learning (FL) is a promising technique for resolving the rising privacy and security concerns. Its main ingredient is to cooperatively learn the model among the distributed clients without uploading any sensitive data. In this paper, we conducted a thorough review of the related works, following the development context and deeply mining the key technologies behind FL from the perspectives of theory and application. Specifically, we first classify the existing works in FL architecture based on the network topology of FL systems with detailed analysis and summarization. Next, we abstract the current application problems, summarize the general techniques and frame the application problems into the general paradigm of FL base models. Moreover, we provide our proposed solutions for model training via FL. We have summarized and analyzed the existing FedOpt algorithms, and deeply revealed the algorithmic development principles of many first-order algorithms in depth, proposing a more generalized algorithm design framework. With the instantiation of these frameworks, FedOpt algorithms can be simply developed. As privacy and security is the fundamental requirement in FL, we provide the existing attack scenarios and the defense methods. To the best of our knowledge, we are among the first tier to review the theoretical methodology and propose our strategies since there are very few works surveying the theoretical approaches. Our survey targets motivating the development of high-performance, privacy-preserving, and secure methods to integrate FL into real-world applications.CCS Concepts: • Theory of computation → Theory and algorithms for application domains; • Networks → Network architectures; • Computing methodologies → Distributed computing methodologies; • Security and privacy;

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“…Having the predicted local FL model updates ∆ w k,t , the PS can optimize the beamforming matrices A t and B t to solve Problem (13). Substituting ∆ w k,t , (6), and ( 7) into ( 13), we have min…”

Section: Optimization Of the Beamforming Matricesmentioning

confidence: 99%

Cross-Layer Federated Learning Optimization in MIMO Networks

Wang¹,

Chen²,

Shen³

et al. 2023

Preprint

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In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, an MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as a optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, 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. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments.

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Federated Learning via Over-the-Air Computation With Statistical Channel State Information

Cited by 24 publications

References 18 publications

Design Pattern and Challenges of Federated Learning with Applications in Industrial Control System

Design Pattern and Challenges of Federated Learning with Applications in Industrial Control System

Advancements in Federated Learning: Models, Methods, and Privacy

Cross-Layer Federated Learning Optimization in MIMO Networks

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