Real-time End-to-End Federated Learning: An Automotive Case Study

With the development and the increasing interests in ML/DL fields, companies are eager to utilize these methods to improve their service quality and user experience. Federated Learning has been introduced as an efficient model training approach to distribute and speed up time-consuming model training and preserve user data privacy. However, common Federated Learning methods apply a synchronized protocol to perform model aggregation, which turns out to be inflexible and unable to adapt to rapidly evolving environments and heterogeneous hardware settings in real-world systems. In this paper, we introduce an approach to real-time end-to-end Federated Learning combined with a novel asynchronous model aggregation protocol. We validate our approach in an industrial use case in the automotive domain focusing on steering wheel angle prediction for autonomous driving. Our results show that asynchronous Federated Learning can significantly improve the prediction performance of local edge models and reach the same accuracy level as the centralized machine learning method. Moreover, the approach can reduce the communication overhead, accelerate model training speed and consume real-time streaming data by utilizing a sliding training window, which proves high efficiency when deploying ML/DL components to heterogeneous real-world embedded systems

EdgeFL: A Lightweight Decentralized Federated Learning Framework

Federated Learning (FL) has emerged as a promising approach for collaborative machine learning, addressing data privacy concerns. However, existing FL platforms and frameworks often present challenges for software engineers in terms of complexity, limited customization options, and scalability limitations. In this paper, we introduce EdgeFL, an edge-only lightweight decentralized FL framework, designed to overcome the limitations of centralized aggregation and scalability in FL deployments. By adopting an edge-only model training and aggregation approach, EdgeFL eliminates the need for a central server, enabling seamless scalability across diverse use cases. With a straightforward integration process requiring just four lines of code (LOC), software engineers can easily incorporate FL functionalities into their AI products. Furthermore, EdgeFL offers the flexibility to customize aggregation functions, empowering engineers to adapt them to specific needs. Based on the results, we demonstrate that EdgeFL achieves superior performance compared to existing FL platforms/frameworks. Our results show that EdgeFL reduces weights update latency and enables faster model evolution, enhancing the efficiency of edge devices. Moreover, EdgeFL exhibits improved classification accuracy compared to traditional centralized FL approaches. By leveraging EdgeFL, software engineers can harness the benefits of federated learning while overcoming the challenges associated with existing FL platforms/frameworks

An architecture for enabling A/B experiments in automotive embedded software

A/B experimentation is a known technique for data-driven product development and has demonstrated its value in web-facing businesses. With the digitalisation of the automotive industry, the focus in the industry is shifting towards software. For automotive embedded software to continuously improve, A/B experimentation is considered an important technique. However, the adoption of such a technique is not without challenge. In this paper, we present an architecture to enable A/B testing in automotive embedded software. The design addresses challenges that are unique to the automotive industry in a systematic fashion. Going from hypothesis to practice, our architecture was also applied in practice for running online experiments on a considerable scale. Furthermore, a case study approach was used to compare our proposal with state-of-practice in the automotive industry. We found our architecture design to be relevant and applicable in the efforts of adopting continuous A/B experiments in automotive embedded software.Comment: To appear in the 45th Annual IEEE Conference on Computers, Software and Applications (COMPSAC'2021

Researching Cooperation and Communication in Continuous Software Engineering

Continuous Software Engineering (CSE) - -continuous development and deployment of software - -and DevOps - -the close cooperation or integration of operations and software development - -is about to change how software is developed. Together with the tighter integration of development and operations also with usage this will change coordination and collaboration both between IT professionals and between developers and users. In this short paper, we discuss the CHASE dimension of three core research themes that begin to crystallize in literature. This position paper is intended as a 'call to arms' for the CHASE community to study CSE

Statistical Models for the Analysis of Optimization Algorithms with Benchmark Functions

Frequentist statistical methods, such as hypothesis testing, are standard practice in papers that provide benchmark comparisons. Unfortunately, these methods have often been misused, e.g., without testing for their statistical test assumptions or without controlling for family-wise errors in multiple group comparisons, among several other problems. Bayesian Data Analysis (BDA) addresses many of the previously mentioned shortcomings but its use is not widely spread in the analysis of empirical data in the evolutionary computing community. This paper provides three main contributions. First, we motivate the need for utilizing Bayesian data analysis and provide an overview of this topic. Second, we discuss the practical aspects of BDA to ensure that our models are valid and the results transparent. Finally, we provide five statistical models that can be used to answer multiple research questions. The online appendix provides a step-by-step guide on how to perform the analysis of the models discussed in this paper, including the code for the statistical models, the data transformations and the discussed tables and figures.Comment: In submissio

Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning

Fast and reliable connectivity is essential to enhancing situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Toward Evidence-Based Organizations Lessons from Embedded Systems, Online Games, and the Internet of Things

Case studies investigated how companies in three domains transition to data-driven development. The results led to a model of the levels that software-intensive companies move through as they evolve from an opinionbased to an evidence-based organization

No More Bosses? : A multi-case study on the emerging use of non-hierarchical principles in large-scale software development

Organizations are increasingly adopting alternative organizational models to circumvent the challenges of traditional hierarchies. In these alternative models, organizations have leaders instead of the traditional boss and teams operate using self-management and peer-to-peer advice processes. Although the adoption of these models have primarily been seen in smaller companies and startups, examples of long-established organizations that have adopted these models to restructure themselves and move away from their traditionally slow hierarchies are starting to appear. In this paper, we explore how seven large software-intensive companies in the embedded systems domain are adopting principles of non-hierarchical organizations in order to increase empowerment. Based on our empirical findings, we provide recommendations for how to manage this transformation and we develop a model that outlines the steps that companies typically take when transforming from hierarchical towards empowered organizations