LeFi: Learn to Incentivize Federated Learning in Automotive Edge Computing

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

Federated learning (FL) is the promising privacy-preserve approach to continually update the central machine learning (ML) model (e.g., object detectors in edge servers) by aggregating the gradients obtained from local observation data in distributed connected and automated vehicles (CAVs). The incentive mechanism is to incentivize individual selfish CAVs to participate in FL towards the improvement of overall model accuracy. It is, however, challenging to design the incentive mechanism, due to the complex correlation between the overall model accuracy and unknown incentive sensitivity of CAVs, especially under the non-independent and identically distributed (Non-IID) data of individual CAVs. In this paper, we propose a new learn-to-incentivize algorithm to adaptively allocate rewards to individual CAVs under unknown sensitivity functions. First, we gradually learn the unknown sensitivity function of individual CAVs with accumulative observations, by using compute-efficient Gaussian process regression (GPR). Second, we iteratively update the reward allocation to individual CAVs with new sampled gradients, derived from GPR. Third, we project the updated reward allocations to comply with the total budget. We evaluate the performance of extensive simulations, where the simulation parameters are obtained from realistic profiling of the CIFAR-10 dataset and NVIDIA RTX 3080 GPU. The results show that our proposed algorithm substantially outperforms existing solutions, in terms of accuracy, scalability, and adaptability.

Identifier

105000826343 (Scopus)

ISBN

[9798350351255]

Publication Title

Proceedings - IEEE Global Communications Conference, GLOBECOM

External Full Text Location

https://doi.org/10.1109/GLOBECOM52923.2024.10900959

e-ISSN

25766813

ISSN

23340983

First Page

1815

Last Page

1820

Grant

2321699

Fund Ref

National Science Foundation

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