TEACHING - Trustworthy autonomous cyber-physical applications through human-centred intelligence

IEEE Trans. Veh. Technol.

Gotta

Valerio

2022

Self Cite

Autonomous vehicles (AVs) generate a massive amount of multi-modal data that once collected and processed through Machine Learning algorithms, enable AI-based services at the Edge. In fact, only a subset of the collected data present informative attributes to be exploited at the Edge. Therefore, extracting such a subset is of utmost importance to limit computation and communication workloads. Doing that in a distributed manner imposes the AVs to cooperate in finding an agreement on which attributes should be sent to the Edge. In this work, we address such a problem by proposing a federated feature selection (FFS) algorithm where the AVs collaborate to filter out, iteratively, the less relevant attributes in a distributed manner, without any exchange of raw data, thought two different components: a Mutual-Information-based feature selection algorithm run by the AVs and a novel aggregation function based on the Bayes theorem executed on the Edge. The FFS algorithm has been tested on two reference datasets: MAV with images and inertial measurements of a monitored vehicle, WESAD with a collection of samples from biophysical sensors to monitor a relative passenger. The numerical results show that the AVs converge to a minimum achievable subset of features with both the datasets, i.e., 24 out of 2166 (99%) in MAV and 4 out of 8 (50%) in WESAD, respectively, preserving the informative content of data.

“…Given that the objective function of problem ( 5) is concave 2 , we can solve it in closed form by imposing:…”

Section: B Ce-based Feature Selection Algorithmmentioning

confidence: 99%

Section: Evaluation Of Federated Feature Selectionmentioning

confidence: 99%

mentioning

confidence: 99%

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Federated Feature Selection for Cyber-Physical Systems of Systems

IEEE Trans. Veh. Technol.

Gotta

Valerio

2022

Self Cite

“…For this reason, we assigned the same size of subset 5000 samples to each client. We use the MNIST dataset 3 , which is a collection of 70k handwritten digits and formed images of size 28x28.…”

Section: A Experimental Environmentmentioning

confidence: 99%

KafkaFed: Two-Tier Federated Learning Communication Architecture for Internet of Vehicles

Bano

Tonellotto

2022 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

et al. 2022

Self Cite

In the current era of the Internet of Vehicles (IoV), vehicle to vehicle data sharing can provide customized applications for Connected and Autonomous Vehicles (CAVs). The advancement of Deep Learning (DL) methodologies is one of the key driving forces for CAVs, allowing elaborating a massive amount of data by the resource-constrained onboard devices. In a traditional centralized DL approach, vehicle data are transmitted to the cloud for the training of models. This approach leads to significant communication overhead, high delays, and data privacy concerns. Conversely, Federated Learning (FL) performs the training using the local models in a distributed fashion and mitigates the data privacy risks by sharing only the model parameters with the server, optimizing the FL to be used with resources-constrained devices. In this paper, we propose the design of a scalable communication infrastructure to support the FL procedure based on Information-Centric Networking (ICN) using Apache Kafka, called KAFKAFED. The ICN-based infrastructure allows to overcome the shortcomings of current client-server architectures for FL, in which routing is contentbased or name-based to achieve efficient data retrieval for mobile nodes. In ICN, data are stored at intermediate nodes to provide efficient and reliable data delivery. A proof of concept of the KAFKAFED communication architecture is developed and tested in an emulated environment. The performance of the proposed framework compared to the client server-based FL architecture, i.e., FLOWER showed a boost of almost 40% with just 32 clients in addition to several other advantages of scalability, reliability, and security Index Terms-Federated Learning, Apache Kafka, Connected and autonomous vehicles, Publish/Subscribe model guarantee user's privacy and to keep the data on user's device, a decentralized approach for training AI models is proposed byMcMahan in 2016, called Federated Learning (FL) [5].In FL, interested clients collaboratively train a model on their local information and exchange their model parameters with the central server to generate a global model shared among all the involved entities [6]. However, to simulate the concept of FL in real-world scenarios is challenging for researchers because they lack the necessary resources to train their federated models on millions of real-world devices, except for a few who work for companies like Google and Facebook. Nevertheless, since the concept has been introduced, there has been a few open-access FL frameworks developed that emulate a federated environment, such as TensorFlow Federated (TFF) 1 introduced by Google, LEAF [7], and FedEval [8]. TFF provide a framework to implement decentralized training, LEAF offers datasets for FL applications, and FedEval provides customized strategy options for communication protocols and aggregation methods for FL. Other frameworks include FedML [9], which provides support for real-world IoT devices by using FedML-Mobile and FedML-IoT, PySyft [10], that offers the so-called remote workers...

“…There is an obvious convergence with the concept of CPSs, that are engineered systems built from, and depend upon, the seamless integration of computation and physical component with and without human intervention. In this paper, we will focus on a specific use case [1,3] involving a smart-vehicle environment, where the principal investigation target is not the autopilot system, but the human perception of the driving style of the autopilot.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Distributed Model Aggregation using Apache Kafka

Bano

Carlini

Proceedings of the 2nd Workshop on Flexible Resource and Application Management on the Edge

et al. 2022

Self Cite

Multi-Access Edge Computing (MEC) is attracting a lot of interest because it complements cloud-based approaches. Indeed, MEC is opening up in the direction of reducing both interaction delays and data sharing, called Cyber-Physical Systems (CPSs). In the near future, edge technologies will be a fundamental tool to better support time-dependent and data-intensive applications. In this context, this work explores existing and emerging platforms for MEC and human-centric applications, and proposes a suitable architecture that can be used in the context of autonomous vehicle systems.The proposed architecture will support scalable communication among sensing devices and edge/cloud computing platforms, as well as orchestrate services for computing, storage, and learning with the use of an Information-centric paradigm such as Apache Kafka CCS CONCEPTS• Networks → Layering; • Human-centered computing → Mobile devices; • Computer systems organization → Architectures.