# FedPCD

Federated aggregation no longer rewrites the global optimization direction to gain fairness. Earlier methods reweight clients, correct conflicting gradients, or treat client losses as symmetric multi-objective common descent, all of which change the global update direction. FedPCD keeps the conventional FedAvg aggregate as the primary objective and individual client updates as secondary constraints, applying only the minimum correction needed when a constraint is violated; across CIFAR-10, CIFAR-100 and Fashion-MNIST it reaches the highest global accuracy in all 18 configurations and the highest worst-10% client accuracy in 17 of them.

Federated aggregation no longer rewrites the global optimization direction to gain fairness. Earlier methods reweight clients, correct conflicting gradients, or treat client losses as symmetric multi-objective common descent, all of which change the global update direction. FedPCD keeps the conventional FedAvg aggregate as the primary objective and individual client updates as secondary constraints, applying only the minimum correction needed when a constraint is violated; across CIFAR-10, CIFAR-100 and Fashion-MNIST it reaches the highest global accuracy in all 18 configurations and the highest worst-10% client accuracy in 17 of them.