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

LiFi: Learn to Incentivize Federated Learning in Automotive Edge Computing