WAFFLE: Weighted Averaging for Personalized Federated Learning

In collaborative or federated learning, model personalization can be a very effective strategy to deal with heterogeneous training data across clients. We introduce WAFFLE (Weighted Averaging For Federated LEarning), a personalized collaborative machine learning algorithm based on SCAFFOLD. SCAFFOLD uses stochastic control variates to converge towards a model close to the globally optimal model even in tasks where the distribution of data and labels across clients is highly skewed. In contrast, WAFFLE uses the Euclidean distance between clients' updates to weigh their individual contributions and thus minimize the trained personalized model loss on the specific agent of interest. Through a series of experiments, we compare our proposed new method to two recent personalized federated learning methods, Weight Erosion and APFL, as well as two global learning methods, federated averaging and SCAFFOLD. We evaluate our method using two categories of non-identical client data distributions (concept shift and label skew) on two benchmark image data sets, MNIST and CIFAR10. Our experiments demonstrate the effectiveness of WAFFLE compared with other methods, as it achieves or improves accuracy with faster convergence.