Distributed Optimization
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Benchmarks
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Most implemented
Federated Optimization in Heterogeneous Networks
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Power Bundle Adjustment for Large-Scale 3D Reconstruction
Secure Distributed Training at Scale
ZOOpt: Toolbox for Derivative-Free Optimization
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Papers
Communication Reduction via Semantic-Based Encoding in DMPC Using LSTMs
The communication demands of distributed model prediction control (DMPC) can overwhelm even advanced wireless communication technologies as agents must exchange a significant amount of information at least once per time …
Distributed OptimizationFirst-order Constrained Trilevel Optimization Over Distributed Networks for Robust Coreset Selection
With the rapid advancement of the Internet of Things (IoT), massive amounts of data are generated across distributed edge networks. Training models on full data incurs significant computational overhead and storage bottl…
Distributed OptimizationContinual LearningWhat's in a Smoothness Constant? Tighter Rates for Local SGD with Bounded Second-order Heterogeneity
Local SGD, also known as Federated Averaging, is a widely used distributed optimization algorithm. Although Local SGD often outperforms alternatives such as Mini-batch SGD in practice, theory still only partially explain…
Distributed OptimizationDecentralized Gradient Descent: Bottleneck Regimes and Budget Complexity
Decentralized gradient descent (DGD) is widely used for solving distributed optimization problems over networks of agents. While its convergence properties are well understood, less is known about the communication and c…
Distributed OptimizationLearning Adaptive Solvers for Distributed Factor Graph Optimization on Matrix Lie Groups
Modern robotic perception increasingly involves large-scale geometric optimization problems distributed across multiple robots or sessions. However, existing distributed solvers often depend on brittle hand tuning and pr…
Distributed OptimizationCan Model Merging Improve Aggregation in DiLoCo?
Model merging techniques, which aggregate independently finetuned models into one to combine their capabilities, have become a topic of significant interest in recent years, with a broad array of methods having been prop…
Distributed Optimization