EFFICIENT FEDERATED DOMAIN TRANSLATION

Abstract

A central theme in federated learning (FL) is the fact that client data distributions are often not independent and identically distributed (IID), which has strong implications on the training process. While most existing FL algorithms focus on the conventional non-IID setting of class imbalance or missing classes across clients, in practice, the distribution differences could be more complex, e.g., changes in class conditional (domain) distributions. In this paper, we consider this complex case in FL wherein each client has access to only one domain distribution. For tasks such as domain generalization, most existing learning algorithms require access to data from multiple clients (i.e., from multiple domains) during training, which is prohibitive in FL. To address this challenge, we propose a federated domain translation method that generates pseudodata for each client which could be useful for multiple downstream learning tasks. We empirically demonstrate that our translation model is more resource-efficient (in terms of both communication and computation) and easier to train in an FL setting than standard domain translation methods. Furthermore, we demonstrate that the learned translation model enables use of state-of-the-art domain generalization methods in a federated setting, which enhances accuracy and robustness to increases in the synchronization period compared to existing methodology.

1. INTRODUCTION

Distribution shift across clients is a well-known challenge in the Federated Learning (FL) community (Huang et al., 2021) . Most existing works have considered this from the perspective of class imbalance or missing classes (i.e., a shift in marginal distribution of classes) across clients, a form of non independent and identically distributed (IID) datasets (Zhao et al., 2018) . In particular, these works typically assume implicitly that the class conditional distribution of data is the same. In practice, however, the conditional distributions across different clients could be very different, e.g., in computer vision, there is a shift in the data distribution (specifically, illumination) of images captured during the day versus night irrespective of the class label (Lengyel et al., 2021) . This can lead to significant model generalization errors even if we solve the issue of class shifts. Translating between datasets is one promising strategy for mitigating the more general shift across distributions of different clients. Moreover, it could solve the problem of Domain Generalization (DG) which requires a model to generalize to unseen domains (Nguyen et al., 2021) . A domain translation model is one that can translate data from different distributions, typically attempting to align the conditional shift across distributions. In centralized settings, many translation methods have been proposed, such as StarGAN (Choi et al., 2018) . However, in FL, domain translation models can be difficult to train because most existing methods require access to data across all domains. Prior literature does not consider this natural setting of federated domain translation where domain datasets are distributed across clients. In this paper, we empirically demonstrate that a naive implementation of state-of-the-art (SOTA) translation models in the FL context indeed performs poorly given communication limitations between the server and clients that often exist in practice (Azam et al., 2022a) . Then, we propose leveraging an iterative translation model, Iterative Naive Barycenter (INB) (Zhou et al., 2022) , which is much more amenable to FL training in terms of communication efficiency and data privacy considerations. We

