Federated Learning (FL) enables collaborative optimization of machine
learning models across decentralized data by aggregating model parameters. Our
approach extends this concept by aggregating "knowledge" derived from models,
instead of model parameters. We present a novel framework called \codream,
where clients collaboratively optimize randomly initialized data using
federated optimization in the input data space, similar to how randomly
initialized model parameters are optimized in FL. Our key insight is that
jointly optimizing this data can effectively capture the properties of the
global data distribution. Sharing knowledge in data space offers numerous
benefits: (1) model-agnostic collaborative learning, i.e., different clients
can have different model architectures; (2) communication that is independent
of the model size, eliminating scalability concerns with model parameters; (3)
compatibility with secure aggregation, thus preserving the privacy benefits of
federated learning; (4) allowing of adaptive optimization of knowledge shared
for personalized learning. We empirically validate \codream on standard FL
tasks, demonstrating competitive performance despite not sharing model
parameters. Our code: this https URL

通过在输入数据空间中进行联合优化，共享模型知识，而不是模型参数，我们提出了一种名为 Codream 的新颖框架，实现了联邦学习中的模型无关协作学习，消除了模型参数的可扩展性问题，同时保留了联邦学习的隐私保护优势，并允许个性化学习的知识自适应优化。

CoDream：以异构模型而非模型交换进行联邦聚合

CoDream: Exchanging dreams instead of models for federated aggregation  with heterogeneous models

Machine learning models are usually evaluated according to the average case
performance on the test set. However, this is not always ideal, because in some
sensitive domains (e.g. autonomous driving), it is the worst case performance
that matters more. In this paper, we are interested in systematic exploration
of the input data space to identify the weakness of the model to be evaluated.
We propose to use an adversarial examiner in the testing stage. Different from
the existing strategy to always give the same (distribution of) test data, the
adversarial examiner will dynamically select the next test data to hand out
based on the testing history so far, with the goal being to undermine the
model's performance. This sequence of test data not only helps us understand
the current model, but also serves as constructive feedback to help improve the
model in the next iteration. We conduct experiments on ShapeNet object
classification. We show that our adversarial examiner can successfully put more
emphasis on the weakness of the model, preventing performance estimates from
being overly optimistic.

本文提出应用对抗性检验器系统性地探索输入数据空间，以识别机器学习模型的弱点，从而避免性能估计过度乐观。通过在 ShapeNet 对象分类上的实验，证明了对抗检验器可以成功地强调模型的弱点。