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Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments. (arXiv:2208.11311v1 [cs.LG])
Aug. 25, 2022, 1:20 a.m. | Rui Song, Dai Liu, Dave Zhenyu Chen, Andreas Festag, Carsten Trinitis, Martin Schulz, Alois Knoll
cs.CR updates on arXiv.org arxiv.org
We introduce a novel federated learning framework, FedD3, which reduces the
overall communication volume and with that opens up the concept of federated
learning to more application scenarios in network-constrained environments. It
achieves this by leveraging local dataset distillation instead of traditional
learning approaches (i) to significantly reduce communication volumes and (ii)
to limit transfers to one-shot communication, rather than iterative multiway
communication. Instead of sharing model updates, as in other federated learning
approaches, FedD3 allows the connected clients to …
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