Hiro Ishii

FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters

We propose Federated Preconditioned Mixing (FedPM), a novel Federated Learning (FL) method that leverages second-order optimization. Prior methods - such as LocalNewton, LTDA, and FedSophia - have incorporated second-order optimization in FL by performing iterative local updates on clients and apply