Adaptive Model Aggregation for Decentralized Federated Learning in Vehicular Networks

Abstract

In vehicular networks, Decentralized Federated Learning (DFL) has emerged as a cutting-edge methodology for the collaborative development of machine learning models, offering a unique balance between collaborative learning and individual data privacy. This approach is particularly apt in scenarios where vehicles, equipped with their own data and computation capabilities, work together to build a comprehensive and shared global model. Each vehicle contributes to this goal by training local models and merging them through model aggregation with data from nearby vehicles. A notable challenge in this context arises from vehicular networks' characteristics - primarily, their high mobility and the often unreliable nature of wireless communications. These factors frequently result in vehicles receiving only fragments of models from their peers, presenting a complex challenge: how to effectively leverage these incomplete models to accelerate the overall training process while simultaneously managing to keep the computational and communication overhead to a minimum. This thesis introduces the Adaptive Model Aggregation (AMA) algorithm to meet this challenge. AMA functions asynchronously within each vehicle and utilizes a threshold-based mechanism to decide whether to include the partially received models in the aggregation process. Given the variable nature of vehicular networks, it becomes evident that a fixed threshold is insufficient. Consequently, this thesis innovates an algorithm inspired by Contextual Multi-Arm Bandit (CMAB) theory, adapting the threshold for each vehicle based on the current communication dynamics of the network. The efficacy of AMA is tested through a series of comprehensive evaluations conducted within simulated environments, which account for various vehicular mobility patterns. These evaluations provide robust evidence that AMA substantially reduces the overhead associated with DFL aggregation - by as much as 83% - compared to traditional, non-adaptive aggregation methods, without adversely affecting the accuracy of the training process.