research
∙
06/19/2023
Adaptive Federated Learning with Auto-Tuned Clients
Federated learning (FL) is a distributed machine learning framework wher...
share
research
∙
05/20/2023
On First-Order Meta-Reinforcement Learning with Moreau Envelopes
Meta-Reinforcement Learning (MRL) is a promising framework for training ...
research
∙
10/03/2022
PersA-FL: Personalized Asynchronous Federated Learning
We study the personalized federated learning problem under asynchronous ...
research
∙
10/03/2022
Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation
Synchronous updates may compromise the efficiency of cross-device federa...
research
∙
04/18/2022
On Arbitrary Compression for Decentralized Consensus and Stochastic Optimization over Directed Networks
We study the decentralized consensus and stochastic optimization problem...
research
∙
03/22/2022
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography
We propose a distributed Quantum State Tomography (QST) protocol, named ...
research
∙
09/14/2021
Scalable Average Consensus with Compressed Communications
We propose a new decentralized average consensus algorithm with compress...
research
∙
06/16/2021
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs
We present a novel, practical, and provable approach for solving diagona...
research
∙
02/14/2021
Communication-Efficient Distributed Cooperative Learning with Compressed Beliefs
We study the problem of distributed cooperative learning, where a group ...
research
∙
11/14/2020
MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling
Boosting methods are among the best general-purpose and off-the-shelf ma...
research
∙
03/25/2020