arXiv:2409.04111v2 Announce Type: replace Abstract: Vertical federated learning is a natural and elegant approach to integrate multi-view data vertically partitioned across devices (clients) while preserving their privacies. Apart from the model training, existing methods requires the collaboration of all clients in the model inference. However, the model inference is probably maintained for service in a long time, while the collaboration, especially when the clients belong to different organizations, is unpredictable in real-world scenarios, such as concellation of contract, network unavailab
This paper addresses critical challenges in vertical federated learning, particularly the need for continuous client collaboration during inference, which is a practical hurdle in real-world, multi-organizational deployments.
It offers a solution to enhance the practicality and robustness of privacy-preserving AI models in distributed data environments, crucial for sectors dealing with sensitive, siloed information.
The proposed active-passive federated learning approach allows for more flexible and resilient model inference, reducing dependency on all clients being perpetually active.
- · Organizations with siloed multi-view data (e.g., healthcare, finance)
- · Privacy-preserving AI solution providers
- · Federated learning platforms
- · Traditional centralized data integration approaches
- · Federated learning methods requiring strict real-time multi-party collaboration
Improved deployability and adoption of privacy-preserving AI across various industries.
Increased utilization of multi-view datasets that were previously too complex or sensitive to integrate.
Acceleration of secure data collaboration models, potentially leading to new business models around shared, yet private, insights.
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Read at arXiv cs.LG