FedPCD: Priority-Constrained Federated Aggregation
FedPCD introduces a method to improve fairness in federated learning by balancing global model utility with individual client performance needs. It uses Priority-Constrained Descent (PCD) to keep the standard FedAvg update as primary while making only minimal necessary corrections to satisfy specific client progress constraints, leading to superior accuracy and faster convergence.