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Federated learning (FL) allows edge devices to collectively learn a modelwithout directly sharing data within each device, thus preserving privacy andeliminating the need to store data globally. While there are promising resultsunder the assumption of independent and identically distributed (iid) localdata, current state-of-the-art algorithms suffer from performance degradationas the heterogeneity of local data across clients increases. To resolve thisissue, we propose a simple framework, Mean Augmented Federated Learning (MAFL),where clients send and receive averaged local data, subject to the privacyrequirements of target applications. Under our framework, we propose a newaugmentation algorithm, named FedMix, which is inspired by a phenomenal yetsimple data augmentation method, Mixup, but does not require local raw data tobe directly shared among devices. Our method shows greatly improved performancein the standard benchmark datasets of FL, under highly non-iid federatedsettings, compared to conventional algorithms.

FedMix: Approximation of Mixup under Mean Augmented Federated Learning