3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 3 cited
TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation
Laurent Condat, Ivan Agarský, Grigory Malinovsky +1
In distributed optimization and federated learning, slow and costly communication between parallel devices and the central server constitutes the primary bottleneck. To alleviate t…
cs.LG2022
CompressedScaffnew: The First Theoretical Double Acceleration of Communication from Local Training and Compression in Distributed Optimization
Laurent Condat, Ivan Agarský, Peter Richtárik
In distributed optimization, a large number of machines alternate between local computations and communication with a coordinating server. Communication, which can be slow and cost…