A Safe Decentralized Graph Federated Learning Method

Abstract

The invention discloses a safe decentralized graph federation learning method. It includes the following steps: S1: number all clients in sequence, initialize the neural network model parameters of the graph and send the ring communication topology to all clients; S2: each client determines the first-level neighbor client according to the ring communication topology; Neighborhood client, and negotiate with each corresponding second-level neighbor client to generate a corresponding shared key; S3: Each client trains the local graph neural network model and updates the local graph neural network model parameters; S4: Each client receives the graph neural network model parameters sent by the first-level neighbor client to update the local graph neural network model; S5: Repeat step S3-step S4 until the graph neural network model converges. The invention can protect the data privacy and safety of each client, relieve communication bottleneck and reduce communication time.

Description

technical field [0001] The invention relates to the technical field of graph federated learning, in particular to a secure decentralized graph federated learning method. Background technique [0002] In the past few years, the rise and application of neural networks have successfully promoted the research of pattern recognition and data mining. Traditional deep learning methods have achieved great success in extracting Euclidean space data features, but many of the data in the actual scene are generated from non-Euclidean spaces, and the performance of deep learning methods on such data is unsatisfactory. , as shown in the figure, the number of neighbor nodes of each node in the network is not fixed, resulting in some important operations (such as convolution) are easy to calculate on the image, but it is no longer suitable for direct use in the graph. Moreover, deep learning is based on the assumption that the training data satisfies independent and identical distribution,...

AI Technical Summary

Problems solved by technology

The above solution does not protect the local model parameters sent to the central server in the global model aggregation stage to prevent possible information leakage; secondly, the central server responsible for aggregating model information requires a trusted neutral third party Institutions, for modeling between institutions, it is difficult to guarantee such a trusted neutral third party; finally, this centralized architecture puts high demands on the IO capabilities of the central server, and all clients must Waiting for all clients to upload the model parameters to the central server successfully, and then the central server distributes the updated global model parameters to the clients before the client can proceed to the next cycle, which is undoubtedly very time-consuming

Images