Federated Multi-Source Domain Adaptation through Optimal Transport: A Privacy-Preserving Approach for Collaborative Learning

Core Concepts

The core message of this paper is to propose a novel framework called Federated Multi-source Domain Adaptation through Optimal Transport (FMDA-OT) that combines optimal transport and federated learning to enable privacy-preserving multi-source domain adaptation without direct access to source domain data.

Abstract

This paper introduces a two-phase approach for multi-source domain adaptation that preserves data privacy: Optimal Transport Phase: Each source domain client individually applies optimal transport to project its data into the target domain space using the Sinkhorn algorithm with L1L2 class regularization. The quality of the new representation is assessed by testing on a small pseudo-labeled portion of the target domain data available to each client. If the new representation improves performance, the client uses the transported data for the next phase; otherwise, the original source data is retained. Federated Learning Phase: The server aggregates the models from all clients (source domains) using a weighted FedAvg algorithm, where the weights are proportional to the performance of each client model on the pseudo-labeled target data. This allows the server to guide the adaptation process and fine-tune the final model without accessing the source domain data, preserving privacy. The pseudo-labeling of the target domain validation data is performed using spectral clustering and hierarchical optimal transport (HOT) to project the random cluster labels to the real class labels. The proposed FMDA-OT framework addresses the challenges of multi-source domain adaptation while maintaining data privacy, a crucial requirement in many real-world applications. The experiments on benchmark datasets demonstrate the effectiveness of the approach compared to state-of-the-art methods.

Stats

The target domain data is assumed to be unlabeled, while the source domain data is labeled. The target domain validation data is pseudo-labeled using spectral clustering and hierarchical optimal transport. The number of samples in each source domain is not necessarily equal, and the distribution of the domains may differ.

Quotes

"Federated learning enables us to jointly train a machine learning model through the collaboration of multiple parties (i.e. devices, organisations ...etc.) without exchanging the local data." "Unsupervised domain adaptation methods relying on optimal transport have gained attention due to the recent success of optimal transport in diverse machine learning problems."

Key Insights Distilled From

FMDA-OT

by Omar... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06599.pdf

Deeper Inquiries

How can the proposed FMDA-OT framework be extended to handle dynamic changes in the source and target domains over time

To handle dynamic changes in the source and target domains over time, the FMDA-OT framework can be extended by incorporating adaptive learning mechanisms. One approach could involve implementing a feedback loop where the model continuously evaluates its performance on the target domain and adjusts its adaptation strategies accordingly. This feedback loop could trigger re-evaluation of the optimal transport mappings and the federated learning process based on the evolving characteristics of the domains. Additionally, integrating online learning techniques that allow the model to adapt in real-time to changes in the data distribution could enhance the framework's ability to handle dynamic domain shifts. By continuously monitoring the performance metrics and updating the adaptation strategies, the FMDA-OT framework can effectively adapt to changes in the source and target domains over time.

What other techniques, besides optimal transport, could be explored to improve the quality of the new representation of the source domain data

In addition to optimal transport, several other techniques could be explored to enhance the quality of the new representation of the source domain data in the FMDA-OT framework. One promising approach is to incorporate deep generative models, such as variational autoencoders (VAEs) or generative adversarial networks (GANs), to learn a more informative and discriminative latent space representation of the data. These generative models can help capture complex relationships and dependencies in the data, leading to a more effective domain adaptation process. Furthermore, techniques like self-supervised learning and contrastive learning can be utilized to learn robust representations that capture semantic similarities and differences between domains. By leveraging these additional techniques in conjunction with optimal transport, the FMDA-OT framework can improve the quality of the new representation of the source domain data and enhance the overall domain adaptation performance.

How can the FMDA-OT framework be adapted to handle multi-task learning scenarios where the source and target domains have different task objectives

Adapting the FMDA-OT framework to handle multi-task learning scenarios with different task objectives in the source and target domains requires a modification of the adaptation process to accommodate the diverse task requirements. One approach is to incorporate task-specific adaptation layers or modules in the model architecture to enable the learning of task-specific features during the adaptation process. By introducing task-specific components, the model can adapt to the unique characteristics of each task while leveraging the shared knowledge across domains. Additionally, employing a multi-head architecture where different heads are responsible for different tasks can facilitate the simultaneous learning of multiple tasks. By designing the federated learning process to consider task-specific objectives and incorporating task-specific adaptation mechanisms, the FMDA-OT framework can effectively handle multi-task learning scenarios with varying task objectives in the source and target domains.