The Secondary Isolated Data Island: Isolated Data Island Caused by Blockchain in Federated Learning

Compared to other fields, data in the medical field has a higher degree of sensitivity. Therefore, in order to ensure that when using medical data for machine learning, a Federated Learning (FL) framework is usually used. However, the traditional centralized FL framework greatly increases the risk of leaking privacy of clients. For this reason, many researchers have developed and designed a Federated Learning Based on Blockchain (FLchain) to enhance the privacy of this framework. Usually, each hospital will use an independent FLchain. But the researchers overlooked a crucial issue: the application of blockchain in FLchain can create isolated data island. Different FLchain use different blockchains, so the data between the two frameworks cannot be used to train a model at the same time. We call this problem the secondary isolated data island (SIDI). The problem leads to hospitals using FLchain architecture being unable to use each other’s data for model training while ensuring information security. We first discovered and proposed the issue of SIDI, and solved it by using Federated Learning Based on Blockchain with Inter-Blockchain Technology (IBT-FLchain) framework. We designed an Hashed Timelock Contract of global model aggregation (HTLC-M) in FLchain by improving the Hashed Timelock Contract (HTLC). We have demonstrated through experiments that the IBT-FLchain framework can effectively solve SIDI problem and greatly alleviate the storage cost of clients in the blockchain.