117 citations · 411 across the 25 of their papers we have counts for
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2 papers · 1 filter
cs.LG2023★ 1 cited
Quantize Once, Train Fast: Allreduce-Compatible Compression with Provable Guarantees
Jihao Xin, Marco Canini, Peter Richtárik +1
Distributed training enables large-scale deep learning, but suffers from high communication overhead, especially as models and datasets grow. Gradient compression, particularly qua…
cs.LG2023★ 1 cited
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
Fares Fourati, Salma Kharrat, Vaneet Aggarwal +2
Federated learning, an emerging machine learning paradigm, enables clients to collaboratively train a model without exchanging local data. Clients participating in the training pro…