Federated Learning in Vehicular Networks

Elbir, Ahmet M.; Soner, Burak; Coleri, Sinem; Gündüz, Deniz; Bennis, Mehdi

doi:10.48550/arxiv.2006.01412

Cited by 19 publications

(23 citation statements)

References 13 publications

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“…In recent years, FL methods have seemed to solve this technical challenge, even in the field of vehicular networks [5]. The data shared by AVs is crucially sensitive as it can reveal our origins and destinations, even along with timing information; hence, our daily routine.…”

Section: Literature Reviewmentioning

confidence: 99%

The Conceptual Framework of a Privacy-Aware Federated Data Collecting and Learning System

Alekszejenkó¹,

Dobrowiecki²

2022

Proceedings of the 29th Minisymposium

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The federated learning methods offer a strong background of fusing and publishing simultaneously collected data. One of the most challenging problems in federated learning is to hide the identity of the participants. Privacy-preserving techniques try to perturb the participants' local data to match its distribution to the global data.In this paper, we consider that agents collect local environmental data. Neighboring agents can share some of their raw data to support real-time decisions and reduce deviation from the global data distribution. The agents will fuse their collected data into a global model that supports the long-term decision and plan making.We assume the specific situation where the necessary communication protocol between the agents may lead to sharing too much local raw data uncovering private and sensitive attributes of the data sharers.To handle privacy issues, we introduce a privacy-aware framework. Within this framework, local participants balance the amount of the shared raw data to make it informative enough yet not revealing, effectively bounding the loss of privacy. In this study, we use autonomous vehicle agents as an example to demonstrate the concepts of the proposed framework.

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Section: Literature Reviewmentioning

confidence: 99%

The Conceptual Framework of a Privacy-Aware Federated Data Collecting and Learning System

Alekszejenkó¹,

Dobrowiecki²

2022

Proceedings of the 29th Minisymposium

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“…However, these schemes are specifically designed for the topology and dynamics of standard wireless/cellular networks with high node density but relatively low mobility. In contrast, vehicular networks have rather low node density and very high node mobility [19]. As a result, new schemes are required for FL on the road.…”

Section: B Related Workmentioning

confidence: 99%

Clustered Vehicular Federated Learning: Process and Optimization

Taïk¹,

Mlika²,

Cherkaoui³

2022

Preprint

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Federated Learning (FL) is expected to play a prominent role for privacy-preserving machine learning (ML) in autonomous vehicles. FL involves the collaborative training of a single ML model among edge devices on their distributed datasets while keeping data locally. While FL requires less communication compared to classical distributed learning, it remains hard to scale for large models. In vehicular networks, FL must be adapted to the limited communication resources, the mobility of the edge nodes, and the statistical heterogeneity of data distributions. Indeed, a judicious utilization of the communication resources alongside new perceptive learning-oriented methods are vital. To this end, we propose a new architecture for vehicular FL and corresponding learning and scheduling processes. The architecture utilizes vehicular-to-vehicular(V2V) resources to bypass the communication bottleneck where clusters of vehicles train models simultaneously and only the aggregate of each cluster is sent to the multi-access edge (MEC) server. The cluster formation is adapted for single and multi-task learning, and takes into account both communication and learning aspects. We show through simulations that the proposed process is capable of improving the learning accuracy in several non-independent and-identicallydistributed (non-i.i.d) and unbalanced datasets distributions, under mobility constraints, in comparison to standard FL.

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“…1) FL for Vehicular Traffic Planning: Many FL-based architectures have been proposed to support vehicular traffic planning which is an important service in ITS for traffic prediction and vehicle control for congestion minimization. For example, FL is considered in [131] to replace traditional centralized ML approaches in traffic prediction tasks by running ML models directly at the edge devices, e.g., vehicles, based on their datasets such as road geometry, traffic flow and weather. Another privacy-aware traffic prediction solution is studied in [10] where multiple entities such as government, companies, stations join to run a Gated Recurrent Unit neural network (FedGRU) locally to estimate traffic flow and then calculate local updates for aggregation at a data center.…”

Section: B Fl For Smart Transportationmentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen,

Ding,

Pathirana

et al. 2021

Preprint

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The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

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Federated Learning in Vehicular Networks

Cited by 19 publications

References 13 publications

The Conceptual Framework of a Privacy-Aware Federated Data Collecting and Learning System

The Conceptual Framework of a Privacy-Aware Federated Data Collecting and Learning System

Clustered Vehicular Federated Learning: Process and Optimization

Federated Learning for Internet of Things: A Comprehensive Survey

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