Joint Optimal Quantization and Aggregation of Federated Learning Scheme in VANETs

Yifei Li, Yijia Guo, Mamoun Alazab, Shengbo Chen, Cong Shen, Keping Yu

    Research output: Contribution to journalArticlepeer-review


    Vehicular ad hoc networks (VANETs) is one of the most promising approaches for the Intelligent Transportation Systems (ITS). With the rapid increase in the amount of traffic data, deep learning based algorithms have been used extensively in VANETs. The recently proposed federated learning is an attractive candidate for collaborative machine learning where instead of transferring a plethora of data to a centralized server, all clients train their respective local models and upload them to the server for model aggregation. Model quantization is an effective approach to address the communication efficiency issue in federated learning, and yet existing studies largely assume homogeneous quantization for all clients. However, in reality, clients are predominantly heterogeneous, where they support different quantization precision levels. In this work, we propose FedDO - Federated Learning with Double Optimization. Minimizing the drift term in the convergence analysis, which is a weighted sum of squared quantization errors (SQE) over all clients, leads to a double optimization at both clients and server sides. In particular, each client adopts a fully distributed, instantaneous (per learning round) and individualized (per client) quantization scheme that minimizes its own squared quantization error, and the server computes the aggregation weights that minimize the weighted sum of squared quantization errors over all clients. We show via numerical experiments that the minimal-SQE quantizer has a better performance than a widely adopted linear quantizer for federated learning. We also demonstrate the performance advantages of FedDO over the vanilla FedAvg with standard equal weights and linear quantization.

    Original languageEnglish
    Pages (from-to)19852-19863
    Number of pages12
    JournalIEEE Transactions on Intelligent Transportation Systems
    Issue number10
    Early online date2022
    Publication statusPublished - 1 Oct 2022


    Dive into the research topics of 'Joint Optimal Quantization and Aggregation of Federated Learning Scheme in VANETs'. Together they form a unique fingerprint.

    Cite this