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On the Benefits of Multiple Gossip Steps in Communication-Constrained
  Decentralized Optimization

On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization

20 November 2020
Abolfazl Hashemi
Anish Acharya
Rudrajit Das
H. Vikalo
Sujay Sanghavi
Inderjit Dhillon
ArXivPDFHTML

Papers citing "On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization"

3 / 3 papers shown
Title
Faster Non-Convex Federated Learning via Global and Local Momentum
Faster Non-Convex Federated Learning via Global and Local Momentum
Rudrajit Das
Anish Acharya
Abolfazl Hashemi
Sujay Sanghavi
Inderjit S. Dhillon
Ufuk Topcu
FedML
35
82
0
07 Dec 2020
FedPAQ: A Communication-Efficient Federated Learning Method with
  Periodic Averaging and Quantization
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Amirhossein Reisizadeh
Aryan Mokhtari
Hamed Hassani
Ali Jadbabaie
Ramtin Pedarsani
FedML
174
760
0
28 Sep 2019
Linear Convergence of Gradient and Proximal-Gradient Methods Under the
  Polyak-Łojasiewicz Condition
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi
J. Nutini
Mark W. Schmidt
139
1,199
0
16 Aug 2016
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