Privacy-Preserved Federated Learning for Autonomous Driving

Li, Yijing; Tao, Xiaofeng; Zhang, Xuefei; Liu, Junjie; Xu, Jin

doi:10.1109/tits.2021.3081560

Cited by 125 publications

(58 citation statements)

References 41 publications

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“…FL is beneficial to many scenarios in ITS or IoV, e.g. optimized routing, congestion control and object detection for autonomous driving ( [37], [44], [49], [61], [63]). Vehicles collect local information and train local models with collected data.…”

Section: Rq 1: Overviewmentioning

confidence: 99%

“…Even though expansive, since all blockchain nodes store the same information in a redundant fashion, it might make sense to capitalize on the immutability and transparency feature of blockchain and store information where either a shared access among participants is required or where verifiability of the history is required to hold agents accountable for posterior reward calculations [36]. In particular, machine learning models [31], [37], [40], [44], [46], [47], [52], [53], [54], [58], [59], reputation scores [36], [42], User-information [30], [31], [38], [39], [58], [59] and Votes [30], [58], [59] are stored on-chain of the respective FLF.…”

Section: Rq 2-2: How Is Blockchain Applied Within the Flf And What Op...mentioning

confidence: 99%

“…From our study, validators can be classified into (i) aggregation servers (e.g. [33], [37], [44]), (ii) task requesters (e.g. [36], [45], [56]), (iii) validators whose task is only to measure contribution ( [39], [51]), (iv) blockchain nodes (e.g.…”

Section: Rq 3-2: How Are the Contributions Of Workers Measured?mentioning

confidence: 99%

See 2 more Smart Citations

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt¹,

Heyer²,

Toyoda³

et al. 2022

Preprint

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The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mobile and edge devices without data leaving the respective device, ensuring privacy by design. Yet, in order to scale this new paradigm beyond small groups of already entrusted entities towards mass adoption, the Federated Learning Framework (FLF) has to become (i) truly decentralized and (ii) participants have to be incentivized. This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning. 422 publications were retrieved, by querying 12 major scientific databases. Finally, 40 articles remained after a systematic review and filtering process for in-depth examination. Although having massive potential to direct the future of a more distributed and secure AI, none of the analyzed FLF is production-ready. The approaches vary heavily in terms of use-cases, system design, solved issues and thoroughness. We are the first to provide a systematic approach to classify and quantify differences between FLF, exposing limitations of current works and derive future directions for research in this novel domain.

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Section: Rq 1: Overviewmentioning

confidence: 99%

Section: Rq 2-2: How Is Blockchain Applied Within the Flf And What Op...mentioning

confidence: 99%

See 1 more Smart Citation

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt¹,

Heyer²,

Toyoda³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…FL also contributes to enhancing privacy, which is a critical issue in communications. FL applications in communications [35] include autonomous driving [36], unmanned aerial vehicle (UAV)based wireless networks [37], edge computing [38], physical layer optimization [39], or Internet-of-Things (IoT) intelligence [40].…”

Section: An Introduction To Fed-erated Learning For Net-workingmentioning

confidence: 99%

Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs

Wilhelmi¹,

Hribar²,

Yılmaz³

et al. 2022

Preprint

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As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency. To unleash the potential of such new features, artificial intelligence (AI) and machine learning (ML) are currently being exploited for deriving models and protocols from data, rather than by hand-programming. In this paper, we explore the feasibility of applying ML in next-generation wireless local area networks (WLANs). More specifically, we focus on the IEEE 802.11ax spatial reuse (SR) problem and predict its performance through federated learning (FL) models. The set of FL solutions overviewed in this work is part of the 2021 International Telecommunication Union (ITU) AI for 5G Challenge.

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“…Corresponding author: lidia.fantauzzo@studenti.polito.it Other authors: name.surname@polito.it 2 Fabio Cermelli is with Italian Institute of Technology, Genoa, Italy. personal and privacy-protected information, thus violating the regulations in force [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

Fantauzzo¹,

Fani'²,

Caldarola³

et al. 2022

Preprint

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Semantic Segmentation is essential to make selfdriving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. However, it operates on sensible data collected from the users' cars; thus, protecting the clients' privacy becomes a primary concern. For similar reasons, Federated Learning has been recently introduced as a new machine learning paradigm aiming to learn a global model while preserving privacy and leveraging data on millions of remote devices. Despite several efforts on this topic, no work has explicitly addressed the challenges of federated learning in semantic segmentation for driving so far. To fill this gap, we propose FedDrive, a new benchmark consisting of three settings and two datasets, incorporating the real-world challenges of statistical heterogeneity and domain generalization. We benchmark state-of-the-art algorithms from the federated learning literature through an in-depth analysis, combining them with style transfer methods to improve their generalization ability. We demonstrate that correctly handling normalization statistics is crucial to deal with the aforementioned challenges. Furthermore, style transfer improves performance when dealing with significant appearance shifts. We plan to make both the code and the benchmark publicly available to the research community.

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Privacy-Preserved Federated Learning for Autonomous Driving

Cited by 125 publications

References 41 publications

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs

FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

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