A Federated Learning protocol built on Proof of Stake to establish trust and organisation reliability in the society. The architecture is built on top of micro-rollups to provide verifiable off-chain computation for state management
Federated Learning and its Problems Federated Learning is a privacy preserving scheme to train deep learning models. Data exists in isolated pools and clients that are part of the network train a model with base parameters on their own individual data. They share the updated model parameters with an aggregator that takes the federated average of this set of models. The result is going to be a new updated base model for the next epoch of training.
In a network of clients, you have to ensure that they are training models honestly so that the accuracy of the model improves. You can have malicious clients in a network that can sabotage the network and reduce model accuracy. We can solve this problem by leveraging a Proof of Stake architecture.
A user can onboard on our platform and require a particular type of model firstly, registering as an user and then staking some Sol. Then they get the permission to download the client part of the global model which they would feed their datat to train. While training, their wallet address is asked as a method of authentication and the hash256 is matched with the wallet address of the user in the web. When the model is trained and user is authenticated, they are eligible to receive sol in their waller