ScalingFL

It takes the gradient parameters from the clients taking part in the learning process, aggregates it trustlessly using ZK, and then sends the updated Params back to the clients. In the end, we have a differentially private distributed learning system with a trustless server.

Verifier contract deployed on Linea testnet 0xd1998ca0000f01442f54ca8bf35017f43aa6ef26

Verifier Contract deployed on Polygon zkEVM Cardona testnet 0xe7f9a7D7945aCfa9180c60c2A4A8669566471faE

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