Blockchain-based secure federated learning

One or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause a system to perform operations, the operations comprising:

• publishing metadata to a blockchain, the metadata describing a training task associated with a global machine-learning model and one or more computational resource requirements for performing the training task;
• receiving a request to participate in training the global machine-learning model from each client of a plurality of clients in which each request is made by a respective client that includes a respective local dataset based on a self-analysis, by the respective client, of a relevance of the respective local dataset to the training task and a suitability of the respective client to the computational resource requirements for performing the training task;
• obtaining a plurality of local model updates for the global machine-learning model in which each respective local model update of the plurality of local model updates corresponds to a respective client of the plurality of clients and each respective local model update is generated based on training, by the respective client, of the global machine-learning model with a local dataset associated with the respective client;
• aggregating the plurality of local model updates to obtain an aggregated local model update;
• and
• generating an updated global machine-learning model based on the aggregated local model update.

A system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause the system to perform operations, the operations comprising:
• publishing metadata to a blockchain, the metadata describing a training task associated with a global machine-learning model and one or more computational resource requirements for performing the training task;
• receiving a request to participate in training the global machine-learning model from each client of a plurality of clients in which each request is made by a respective client that includes a respective local dataset based on a self-analysis, by the respective client, of a relevance of the respective local dataset to the training task and a suitability of the respective client to the computational resource requirements for performing the training task;
• obtaining a plurality of local model updates for the global machine-learning model in which each respective local model update of the plurality of local model updates corresponds to a respective client of the plurality of clients and each respective local model update is generated based on training, by the respective client, of the global machine-learning model with a local dataset associated with the respective client;
• aggregating the plurality of local model updates to obtain an aggregated local model update;
• and
• generating an updated global machine-learning model based on the aggregated local model update.