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Deep hashing has been widely applied in large-scale data retrieval due to itssuperior retrieval efficiency and low storage cost. However, data are oftenscattered in data silos with privacy concerns, so performing centralized datastorage and retrieval is not always possible. Leveraging the concept offederated learning (FL) to perform deep hashing is a recent research trend.However, existing frameworks mostly rely on the aggregation of the local deephashing models, which are trained by performing similarity learning with localskewed data only. Therefore, they cannot work well for non-IID clients in areal federated environment. To overcome these challenges, we propose a novelfederated hashing framework that enables participating clients to jointly trainthe shared deep hashing model by leveraging the prototypical hash codes foreach class. Globally, the transmission of global prototypes with only oneprototypical hash code per class will minimize the impact of communication costand privacy risk. Locally, the use of global prototypes are maximized byjointly training a discriminator network and the local hashing network.Extensive experiments on benchmark datasets are conducted to demonstrate thatour method can significantly improve the performance of the deep hashing modelin the federated environments with non-IID data distributions.

FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval