Finite-Time Analysis of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning
Author:
Affiliation:
1. Georgia Tech.,Department of Electrical and Computer Engineering
2. Virginia Tech.,Bradley ECE Department
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9682670/9682776/09683363.pdf?arnumber=9683363
Reference25 articles.
1. On Nonconvex Decentralized Gradient Descent
2. A unified theory of decentralized sgd with changing topology and local updates;koloskova,2020
3. Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization
4. Decentralized markov chain gradient descent;sun,2019
5. On the Convergence of Consensus Algorithms with Markovian noise and Gradient Bias
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1. A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning;SIAM Journal on Optimization;2024-03-08
2. Finite-Time Convergence Rates of Decentralized Stochastic Approximation With Applications in Multi-Agent and Multi-Task Learning;IEEE Transactions on Automatic Control;2023-05
3. A reinforcement learning approach to parameter selection for distributed optimal power flow;Electric Power Systems Research;2022-11
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