Federated learning aims to train predictive models for data that is
distributed across clients, under the orchestration of a server. However,
participating clients typically each hold data from a different distribution,
whereby predictive models with strong in-distribution generalization can fail
catastrophically on unseen domains. In this work, we argue that in order to
generalize better across non-i.i.d. clients, it is imperative to only learn
correlations that are stable and invariant across domains. We propose FL Games,
a game-theoretic framework for federated learning for learning causal features
that are invariant across clients. While training to achieve the Nash
equilibrium, the traditional best response strategy suffers from high-frequency
oscillations. We demonstrate that FL Games effectively resolves this challenge
and exhibits smooth performance curves. Further, FL Games scales well in the
number of clients, requires significantly fewer communication rounds, and is
agnostic to device heterogeneity. Through empirical evaluation, we demonstrate
that FL Games achieves high out-of-distribution performance on various
benchmarks.

本文提出了 FL Games，这是一个博弈理论框架，旨在通过学习跨领域不变关系来改善联邦学习中的预测模型泛化，FL Games 在多个基准测试中展现了高超的领域外表现。

FL Games：一种用于分布偏移的联邦学习框架

FL Games: A federated learning framework for distribution shifts

There is an increasing interest in algorithms to learn invariant correlations
across training environments. A big share of the current proposals find
theoretical support in the causality literature but, how useful are they in
practice? The purpose of this note is to propose six linear low-dimensional
problems -- unit tests -- to evaluate different types of out-of-distribution
generalization in a precise manner. Following initial experiments, none of the
three recently proposed alternatives passes all tests. By providing the code to
automatically replicate all the results in this manuscript
(this https URL), we hope that our
unit tests become a standard steppingstone for researchers in
out-of-distribution generalization.

本文提出六种线性低维度问题，即单元测试，以精确评估不同类型的超出分布泛化问题，提供代码以自动复制所有结果，希望我们的单元测试成为超出分布泛化研究人员的标准起点。

线性单元测试用于不变性发现

Linear unit-tests for invariance discovery

We introduce Invariant Risk Minimization (IRM), a learning paradigm to
estimate invariant correlations across multiple training distributions. To
achieve this goal, IRM learns a data representation such that the optimal
classifier, on top of that data representation, matches for all training
distributions. Through theory and experiments, we show how the invariances
learned by IRM relate to the causal structures governing the data and enable
out-of-distribution generalization.

介绍了不变风险最小化 (IRM) 的学习模式，通过学习数据表示来估计多个训练分布之间的不变关系，从而实现在所有训练分布上匹配最优分类器，通过理论和实验表明，IRM 学习到的不变性与控制数据的因果结构相关，并能够实现分布外概括。