Robust Federated Learning with Local Mixed Co-teaching
Girum Fitihamlak Ejigu, Sang Hoon Hong, Choong Seon Hong · 2023
Federated Learning paradigm ensures basic data privacy of local clients through an iterative aggregation of model parameters. The success of a global model in federated learning depends on local models that are trained on self-labeled client data. However, all participating clients have their own personal bias and different expertise level that leads to label noise. Hence, a federated learning model should be robust to noise and deliver consistent output. To deal with this issue, we here propose a robust federated learning approach that focuses on a local model training phase of clients. We simultaneously train two deep networks using normal and augmented inputs and mix up their predicted classes to minimize entropy before using a noise tolerant loss function. Further, we add a simple knowledge distillation technique to enhance the performance of the network. We test our proposed method with CIFAR-10 and Fashion-MNIST datasets in both IID and non-IID data distribution settings to showcase its robustness to noise.