The rise of pre-trained foundation models (FMs) has popularized the trend of fine-tuning FMs to fit downstream tasks, while Federated Learning (FL) has become the de-facto approach for training distributed data with privacy-preservation. However, fine-tuning FMs in FL faces overwhelming overheads due to its bulky nature. While freezing parameters in FM have the potential to accelerate FL training, existing freezing strategies statically freeze parameters on specified or already converged layers, incur severe accuracy degradation, and resource-inefficiency in heterogeneous environments. In this paper, we propose AFedLF, an adaptive freezing framework for FM in FL, to accelerate its wall-clock time for convergence without losing its final accuracy. However, this poses great challenges, as different freezing strategies lead to different accuracy gains and time overheads, while unfreezing more layers may bring marginal accuracy gains but significant time overheads. To address this challenge, AFedLF mathematically establishes a correlation between the freezing strategy and the accuracy gain and time overhead, and allocates adaptive freezing strategies to clients, based on our insight that unfreezing more layers on devices with strong computation and communication capabilities helps improve resource efficiency. Besides, AFedLF incorporates our well-designed intermediate result caching scheme with constant approximation ratios utilizing the limited storage capacity on mobile devices to cache intermediate results to skip forward propagation, further saving wall-clock time. Finally, we implemented AFedLF using an open-source FL benchmark, and extensive trace-driven experimental results showed that AFedLF accelerates wall-clock time by up to 6.1× compared to state-of-the-art solutions, without sacrificing accuracy.

Accurate detection and tracking of traffic participants are crucial to advanced driver assistance systems. This paper presents a centralized object tracking approach for surrounding objects in road traffic environments by using multiple linearly arrayed ultrasonic sensors. An ultrasonic sensor model is specifically developed for traffic environment, which consists of detection scope, chance of detection and ranging error, incorporating factors of object shapes, materials, distances and orientations. A centralized filter is designed to selectively fuse new measurements that are obtained using the Extended Kalman Filter (EKF) from certain sensors to conduct object tracking at each step. The effectiveness of proposed method is validated by simulations, which is found to have superior tracking performance compared to traditional triangle localization method, with more stable and smaller tracking error, especially when the object is entering or leaving the detection area.