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FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang +2
The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improv…
Resource-Constrained On-Device Learning by Dynamic Averaging
Lukas Heppe, Michael Kamp, Linara Adilova +3
The communication between data-generating devices is partially responsible for a growing portion of the world's power consumption. Thus reducing communication is vital, both, from…
Information-Theoretic Perspective of Federated Learning
Linara Adilova, Julia Rosenzweig, Michael Kamp
An approach to distributed machine learning is to train models on local datasets and aggregate these models into a single, stronger model. A popular instance of this form of parall…