Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data
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DOI: 10.1109/tpds.2022.3150579
Abstract: Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data. Considering heterogeneous computing and communication capabilities of different participants, asynchronous FL can avoid… read more here.
Keywords: non iid; iid data; federated learning; stale gradients ... See more keywords