conftrace_

distributed optimization

275 papers

Explore in graph

Co-occurring keywords

federated learning (1333) communication efficiency (297) distributed learning (563) stochastic gradient descent (1091) convex optimization (1321) stochastic optimization (1060) communication complexity (99) variance reduction (523) communication compression (55) non-convex optimization (547)

Papers

Towards Optimal Communication Complexity in Distributed Non-Convex Optimization NIPS 2022
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning NIPS 2022
Federated Learning with Partial Model Personalization ICML 2022
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression NIPS 2022
Distributed Distributionally Robust Optimization with Non-Convex Objectives NIPS 2022
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale ICML 2022
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning AISTATS 2022
Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization AISTATS 2022
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations NIPS 2022
A Unified Analysis of Federated Learning with Arbitrary Client Participation NIPS 2022
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning CVPR 2022
Beyond spectral gap: the role of the topology in decentralized learning NIPS 2022
A Distributed Method for Fitting Laplacian Regularized Stratified Models JMLR 2021
Distributed Second Order Methods with Fast Rates and Compressed Communication ICML 2021
On the Convergence of Communication-Efficient Local SGD for Federated Learning AAAI 2021
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization NIPS 2021
Communication-Efficient Distributed Optimization with Quantized Preconditioners ICML 2021
Newton Method over Networks is Fast up to the Statistical Precision ICML 2021
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices NIPS 2021
Exponential Graph is Provably Efficient for Decentralized Deep Training NIPS 2021
Local SGD: Unified Theory and New Efficient Methods AISTATS 2021
Federated Learning under Arbitrary Communication Patterns ICML 2021
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them JMLR 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients NIPS 2021
LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads AISTATS 2021