Method for building blockchain-based secure aggregation in federated learning with data removal

A method for building blockchain-based secure aggregation in federated learning with data removal, applied by a first client node in a system for building blockchain-based secure aggregation in federated learning with data removal, comprising:

• receiving, from a server in the system, a list of selected client nodes, wherein the selected client nodes are in a first quantity and are selected to participate in an i-th iteration of the federated learning by the server from a plurality of client nodes in the system, each of the selected client nodes is provided with a unique identifier id, and the first client node is one of the first quantity of the selected client nodes, i is an integer;
• acquiring model training information by training a local model with local training data;
• generating cypher text of the model training information by performing homomorphic encryption on the model training information based on pairwise seeds computed for each of the first quantity of client nodes from a symmetric bivariate polynomial and a private seed computed from asymmetric bivariate polynomial;
• and
• sending the cypher text of the model training information to the server.

A system for building blockchain-based secure aggregation in federated learning with data removal, comprising a server, a plurality of client nodes, and a block chain, wherein:

• the server is configured for:
• selecting, from the plurality of client nodes each being provided with a unique identifier id in the system, a first quantity of client nodes, to participate in an i-th iteration of the federated learning, where i is an integer;
• sending a list of the selected client nodes to each of the first quantity of client nodes;
• acquiring model training information transmitted by each of a second quantity of client nodes among the first quantity of client nodes, the model training information being transmitted in a form of cypher text;
• aggregating the cypher text of the model training information to obtain an aggregate result;
• and
• broadcasting a list of the second quantity of client nodes and the aggregate result via the blockchain;
• and
• each of the plurality of client nodes is configured for:
• receiving, from a server in the system, the list of selected first quantity client nodes;
• acquiring model training information by training a local model with local training data;
• generating cypher text of the model training information by performing homomorphic encryption on the model training information based on pairwise seeds computed for each of the first quantity of client nodes from a symmetric bivariate polynomial and a private seed computed from asymmetric bivariate polynomial;
• and
• sending the cypher text of the model training information to the server.