Leveraging the computing and sensing capabilities of vehicles, vehicular
federated learning (VFL) has been applied to edge training for connected
vehicles. The dynamic and interconnected nature of vehicular networks presents
unique opportunities to harness direct vehicle-to-vehicle (V2V) communications,
enhancing VFL training efficiency. In this paper, we formulate a stochastic
optimization problem to optimize the VFL training performance, considering the
energy constraints and mobility of vehicles, and propose a V2V-enhanced dynamic
scheduling (VEDS) algorithm to solve it. The model aggregation requirements of
VFL and the limited transmission time due to mobility result in a stepwise
objective function, which presents challenges in solving the problem. We thus
propose a derivative-based drift-plus-penalty method to convert the long-term
stochastic optimization problem to an online mixed integer nonlinear
programming (MINLP) problem, and provide a theoretical analysis to bound the
performance gap between the online solution and the offline optimal solution.
Further analysis of the scheduling priority reduces the original problem into a
set of convex optimization problems, which are efficiently solved using the
interior-point method. Experimental results demonstrate that compared with the
state-of-the-art benchmarks, the proposed algorithm enhances the image
classification accuracy on the CIFAR-10 dataset by 3.18% and reduces the
average displacement errors on the Argoverse trajectory prediction dataset by
10.21%.

利用车辆的计算和感知能力，本文提出了一种车辆联邦学习（VFL）算法，通过直接车对车（V2V）通信增强 VFL 训练效率，并考虑能量限制和车辆的移动性问题来优化 VFL 训练性能。通过导数漂移加罚方法将长期随机优化问题转化为在线混合整数非线性规划（MINLP）问题，并通过理论分析来衡量在线解和离线最优解之间的性能差距。通过对调度优先级的进一步分析，将原始问题简化为一组凸优化问题，并使用内点法有效求解。实验结果表明，与现有基准相比，所提出的算法将 CIFAR-10 数据集上的图像分类准确度提高了 3.18％，并将 Argoverse 轨迹预测数据集上的平均位移误差降低了 10.21％。

车辆间通信增强的动态调度联邦学习

Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced  Federated Learning

Federated learning (FL) has received significant attention in recent years
for its advantages in efficient training of machine learning models across
distributed clients without disclosing user-sensitive data. Specifically, in
federated edge learning (FEEL) systems, the time-varying nature of wireless
channels introduces inevitable system dynamics in the communication process,
thereby affecting training latency and energy consumption. In this work, we
further consider a streaming data scenario where new training data samples are
randomly generated over time at edge devices. Our goal is to develop a dynamic
scheduling and resource allocation algorithm to address the inherent randomness
in data arrivals and resource availability under long-term energy constraints.
To achieve this, we formulate a stochastic network optimization problem and use
the Lyapunov drift-plus-penalty framework to obtain a dynamic resource
management design. Our proposed algorithm makes adaptive decisions on device
scheduling, computational capacity adjustment, and allocation of bandwidth and
transmit power in every round. We provide convergence analysis for the
considered setting with heterogeneous data and time-varying objective
functions, which supports the rationale behind our proposed scheduling design.
The effectiveness of our scheme is verified through simulation results,
demonstrating improved learning performance and energy efficiency as compared
to baseline schemes.

通过动态调度和资源分配算法解决异构数据和时间变化目标函数下，在长期能量限制条件下，流式数据随机生成和资源可用性的固有随机性，并通过仿真结果验证了所提出方案相对于基准方案的学习性能和能效的提高。

基于流数据的能效联合边缘学习：一种 Lyapunov 优化方法

Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov  Optimization Approach

Current approaches for activity recognition often ignore constraints on
computational resources: 1) they rely on extensive feature computation to
obtain rich descriptors on all frames, and 2) they assume batch-mode access to
the entire test video at once. We propose a new active approach to activity
recognition that prioritizes "what to compute when" in order to make timely
predictions. The main idea is to learn a policy that dynamically schedules the
sequence of features to compute on selected frames of a given test video. In
contrast to traditional static feature selection, our approach continually
re-prioritizes computation based on the accumulated history of observations and
accounts for the transience of those observations in ongoing video. We develop
variants to handle both the batch and streaming settings. On two challenging
datasets, our method provides significantly better accuracy than alternative
techniques for a wide range of computational budgets.

本研究提出了一种新的活动识别方法，可以在不浪费计算资源的情况下及时预测，通过动态调度所选测试视频的帧上计算的特征序列来学习策略，相对于传统的静态特征选择，我们的方法可以持续优化计算，适用于批处理模式和流模式。在两个具有挑战性的数据集上，我们的方法在广泛的计算预算范围内提供了比替代技术更好的准确性。