Federated Learning of Multi-modal Transformers with Complementary Knowledge Sharing and Collaborative Aggregation

Core Concepts

This paper explores a transfer multi-modal federated learning (MFL) scenario within the vision-language domain, where clients possess data of various modalities distributed across different datasets. The authors propose a novel framework called Federated modality Complementary and collaboration (FedCola) to address the in-modality and cross-modality gaps among clients by leveraging the unified design of transformers across different modalities.

Abstract

The paper explores a transfer multi-modal federated learning (MFL) setting in the vision-language domain, where clients possess data of various modalities distributed across different datasets. The authors propose a novel framework called FedCola to address the challenges in this setting.

Key highlights:

1. The transfer MFL setting allows participation of uni-modal clients with unpaired data, in addition to multi-modal clients, which can further extend the scope of training data in federated learning.
2. FedCola consists of two main components:
   a. Complementary local training: Uni-modal clients download transformer blocks from the other modalities and use a gating mechanism to leverage the complementary knowledge during local training, addressing the cross-modality gap.
   b. Collaborative aggregation: The server performs selective aggregation of self-attention layers across modalities, while disaggregating other layers to maintain task-specific knowledge, addressing the in-modality gap.
3. Extensive experiments on real-world datasets under various federated learning settings demonstrate the effectiveness of FedCola, outperforming previous methods.
4. Further analysis shows that FedCola maintains performance on uni-modal tasks while improving multi-modal performance, and the contributions of different client types can be quantified using Shapley value.
5. Visualization of the loss landscape and feature embeddings indicate that FedCola learns a more generalized global model compared to the baseline.

Stats

The total number of clients is N = 32, with Nv = 12 image clients, Nl = 12 text clients, and Nvl = 8 image-text clients.
The local training data is partitioned following a non-IID Dirichlet distribution with α = 0.5.
In each round, r = 0.25 of the clients on each modality type participate in the training and aggregation.

Quotes

"To remedy this, federated learning (FL) has emerged as a promising privacy-preserving paradigm for training models without direct access to the raw data held by different clients."
"Nonetheless, in domains with rich multi-modal data, such as healthcare and the Internet of Things [1,53,60], multi-modal federated learning (MFL) are gaining attention [4]."
"To tackle these challenges, we propose a novel framework for the transfer MFL in both local training and global aggregation, leveraging the unified design of transformers across different modalities."

Key Insights Distilled From

Towards Multi-modal Transformers in Federated Learning

by Guangyu Sun,... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12467.pdf

Deeper Inquiries

How can the proposed framework be extended to handle larger domain gaps between uni-modal and multi-modal clients

To handle larger domain gaps between uni-modal and multi-modal clients, the proposed framework can be extended in several ways. One approach is to introduce domain adaptation techniques to align the feature distributions between different modalities. This can involve incorporating domain-specific adaptation layers or loss functions during the training process to bridge the domain gap. Additionally, utilizing transfer learning methods, such as pre-training on a related dataset with similar characteristics, can help the model generalize better across different domains. Another strategy is to introduce data augmentation techniques specifically tailored to bridge the domain gap, such as generating synthetic data points that mimic the characteristics of the underrepresented modality. By incorporating these strategies, the framework can adapt more effectively to larger domain gaps and improve performance in multi-modal federated learning scenarios.

What are the potential implications of the quantified contributions of different client types in terms of fair profit sharing among participants

The quantified contributions of different client types in terms of fair profit sharing among participants can have significant implications for the overall fairness and equity in federated learning settings. By understanding the Shapley value of each type of uni-modal client, it becomes possible to allocate profits in a more equitable manner based on the actual contributions of each client type to the overall performance. This can lead to a more transparent and fair profit-sharing mechanism, where clients are rewarded based on their specific contributions to the collaborative model. Additionally, this analysis can help in establishing trust and cooperation among participants, as they see the direct correlation between their efforts and the rewards they receive. Fair profit sharing based on quantified contributions can also incentivize active participation and engagement from all client types, leading to more effective and collaborative federated learning processes.

Can the collaborative aggregation strategy be further improved by exploring cross-modal collaboration on the global model

The collaborative aggregation strategy can be further improved by exploring cross-modal collaboration on the global model. By incorporating cross-modal collaboration, the global model can benefit from the diverse knowledge and insights present in different modalities. This can be achieved by designing aggregation mechanisms that allow for information exchange and collaboration between uni-modal and multi-modal clients at the global level. For example, introducing cross-modal attention mechanisms or knowledge distillation techniques can enable the global model to leverage the complementary strengths of different modalities. By fostering collaboration across modalities at the global aggregation stage, the framework can enhance the overall performance and generalizability of the multi-modal model in federated learning scenarios.