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Existing model marketplaces typically trade and select individual models as indivisible units, limiting their ability to exploit complementarities among heterogeneous experts. This paper proposes an MoE-based model market framework that lifts Mixture-of-Experts from a model-level learning architecture to a market-level...
As public concern about data privacy continues to grow, federated learning (FL), as a machine learning technique that does not require the disclosure of user raw data, has attracted widespread attention. However, it still has many common problems. The first problem stems from the absence of incentive mechanisms utilize...
This work presents OMBench through a decision-and-evaluation toolkit with rich visualization features so that users can navigate a set of candidate platforms to meet data management requirements and make informed decisions about their guarantees and resource implications.
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