We introduce a novel approach for adapting deep stereo networks in a
collaborative manner. By building over principles of federated learning, we
develop a distributed framework allowing for demanding the optimization process
to a number of clients deployed in different environments. This makes it
possible, for a deep stereo network running on resourced-constrained devices,
to capitalize on the adaptation process carried out by other instances of the
same architecture, and thus improve its accuracy in challenging environments
even when it cannot carry out adaptation on its own. Experimental results show
how federated adaptation performs equivalently to on-device adaptation, and
even better when dealing with challenging environments.

我们提出了一种新颖的方法，通过在协同学习的基础上构建分布式框架，使得深度立体网络能够从其他相同架构的实例进行自适应过程，从而在不具备自适应能力的资源受限设备上，在具有挑战性的环境中提高准确性。实验结果表明，分布式自适应与设备上自适应相当甚至在处理挑战性环境时更好。

深度立体联邦在线适应

Federated Online Adaptation for Deep Stereo

A primary function of back-propagation is to compute both the gradient of
hidden representations and parameters for optimization with gradient descent.
Training large models requires high computational costs due to their vast
parameter sizes. While Parameter-Efficient Fine-Tuning (PEFT) methods aim to
train smaller auxiliary models to save computational space, they still present
computational overheads, especially in Fine-Tuning as a Service (FTaaS) for
numerous users. We introduce Collaborative Adaptation (ColA) with Gradient
Learning (GL), a parameter-free, model-agnostic fine-tuning approach that
decouples the computation of the gradient of hidden representations and
parameters. In comparison to PEFT methods, ColA facilitates more cost-effective
FTaaS by offloading the computation of the gradient to low-cost devices. We
also provide a theoretical analysis of ColA and experimentally demonstrate that
ColA can perform on par or better than existing PEFT methods on various
benchmarks.

使用协作适应（ColA）和梯度学习（GL）的参数自由、模型不可知的微调方法在性能上与现有的参数高效微调方法相媲美甚至更好，在各种基准测试中 ColA 的计算性能更具成本效益，使得微调作为一项服务可通过将梯度计算卸载到低成本设备来实现。