Federating Hashing Networks Adaptively for Privacy-Preserving Retrieval

Yi Li, Meihua Yu, Xin Xie, Haiyan Fu, Hao He, Yanqing Guo

Abstract: With the rise of neural networks, many deep hashing networks have been successfully trained on the basis of large-scale data. However, the conventional learning process has received increasing challenges from the data privacy concerns and the decentralized storage status, especially in sensitive scenarios like surveillance retrieval. Further considering the probable different distributions of the decentralized data, in this paper, we present a collaborative hashing paradigm FedA-Hash (Federating Adapted Hashing nets) to produce personalized hashing models for the clients without exchanging their local data. To this end, the bilateral knowledge is blended gradually during the learning process between the aggregated global model and the local hashing model, instead of replacing the local model with the global model directly. Extensive experiments are conducted on representative hashing networks, involving tasks as image retrieval and person re-identification. The results show that FedA-Hash significantly enables the collaborated performance among different clients.