Towards Multi-modal Transformers in Federated Learning

• URL: http://arxiv.org/abs/2404.12467v2
• Date: Tue, 16 Jul 2024 21:19:34 GMT
• Title: Towards Multi-modal Transformers in Federated Learning
• Authors: Guangyu Sun, Matias Mendieta, Aritra Dutta, Xin Li, Chen Chen,
• Abstract summary: This paper explores a transfer multi-modal federated learning (MFL) scenario within the vision-language domain. We introduce a novel framework called Federated modality complementary and collaboration (FedCola) by addressing the in-modality and cross-modality gaps among clients.
• Score: 10.823839967671454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal transformers mark significant progress in different domains, but siloed high-quality data hinders their further improvement. 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. Despite its potential, a considerable research direction regarding the unpaired uni-modal clients and the transformer architecture in FL remains unexplored. To fill this gap, 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. We systematically evaluate the performance of existing methods when a transformer architecture is utilized and introduce a novel framework called Federated modality complementary and collaboration (FedCola) by addressing the in-modality and cross-modality gaps among clients. Through extensive experiments across various FL settings, FedCola demonstrates superior performance over previous approaches, offering new perspectives on future federated training of multi-modal transformers.

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