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Distributed Optimization

The goal of Distributed Optimization is to optimize a certain objective defined over millions of billions of data that is distributed over many machines by utilizing the computational power of these machines.

Source: Analysis of Distributed StochasticDual Coordinate Ascent

Papers

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Showing 21–30 of 536 papers

TitleStatusHype
Convergence Theory of Flexible ALADIN for Distributed Optimization—0
Collaborative Satisfaction of Long-Term Spatial Constraints in Multi-Agent Systems: A Distributed Optimization Approach (extended version)—0
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Accelerated Distributed Optimization with Compression and Error Feedback—0
Distributed Pose Graph Optimization using the Splitting Method based on the Alternating Direction Method of Multipliers—0
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges—0
Opportunistic Routing in Wireless Communications via Learnable State-Augmented PoliciesCode0
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs—0
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton—0
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