FLASH · Federated Learning Supporting Efficient and Reliable Inference over Vehicular Networks

FLASH - Federated Learning Supporting Efficient and Reliable Inference over Vehicular Networks

The FLASH project aims to establish the theoretical foundations of machine learning and wireless communications to enable the vision of assisted and self-driving systems. Unfortunately, current systems cannot provide safe and reliable driving because they lack distributed and real-time learning algorithms meeting the critical latency and reliability requirements in highly dynamic and fast-varying wireless channels. Although the fifth generation of cellular systems supports the communication demands for assisted and self-driving, and machine learning proposes federated learning (FL) for distributed scenarios, the wireless communications and machine learning domains are not sufficiently integrated for real-time critical applications. Yet, it is only by their integration that the vision of assisted and self-driving systems will become real. To this end, we will establish a theoretical and algorithmic integration of FL and cellular networks that serve vehicles, which we refer to as FL supporting efficient and reliable inference over vehicular networks (FLASH). FLASH builds on the co-design of a fundamentally new ecosystem in which FL algorithms address critical constraints from vehicular applications, while resource allocation algorithms adapt wireless communication resources to the inference tasks. The goal of this project is to enable assisted and self-driving systems by providing novel theoretical methods on FL and vehicular communications that integrate both paradigms. To reach our goal, we investigate two intertwined research objectives: • Objective 1 [Inception of FLASH for assisted driving systems]: To establish novel theoretical and algorithmic methods for the efficient and reliable integration of FL and vehicular communications for assisted driving systems. Specifically, we will pursue a novel direction by joining theoretical models in the fields of machine learning (federated learning and differential privacy) and wireless communication (finite blocklength theory and physical layer security). The project will proceed in three steps to fulfil this objective. The first step will establish novel theoretical foundations integrating FL and vehicular communications using the finite blocklength theory. The second step will establish strong privacy preserving and/or security guarantees to the theoretical foundations and algorithms established in the first step. These guarantees can be investigated from two perspectives: differential privacy and physical layer security. The third step will consist of the numerical validation of the algorithms from the previous steps using the publicly available datasets for assisted driving systems. • Objective 2 [Extend FLASH to self-driving systems]: To extend the proposed novel theoretical and algorithmic methods of FL and vehicular communications to self-driving systems. Specifically, we intend to integrate transfer learning and model compression to the novel algorithms in Objective 1 to fulfil the more critical requirements of self-driving systems. The project will proceed in two steps to fulfil this objective. The first step will consist of generalizing the novel FLASH theoretical foundations and algorithms established in Objective 1 to autonomous driving systems, with critical latency, reliability, and data rate requirements. The fulfilment of these requirements will use the theories of transfer learning and model compression. The second step will consist of the numerical validation of the first step using public datasets for self-driving systems. Such datasets include the Lyft Level 5 with tasks on traffic detection, motion prediction, and trajectory planning.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз