DiNNO: Distributed Neural Network Optimization for Multi-Robot Collaborative Learning
We present DiNNO, a distributed algorithm that enables a group of robots to collaboratively optimize a deep neural network model while communicating over a mesh network. Each robot only has access to its own data and maintains its own version of the neural network, but eventually learns a model that...
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| Published in: | IEEE robotics and automation letters Vol. 7; no. 2; pp. 1896 - 1903 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Piscataway
IEEE
01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2377-3766, 2377-3766 |
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| Abstract | We present DiNNO, a distributed algorithm that enables a group of robots to collaboratively optimize a deep neural network model while communicating over a mesh network. Each robot only has access to its own data and maintains its own version of the neural network, but eventually learns a model that is as good as if it had been trained on all the data centrally. No robot sends raw data over the wireless network, preserving data privacy and ensuring efficient use of wireless bandwidth. At each iteration, each robot approximately optimizes an augmented Lagrangian function, then communicates the resulting weights to its neighbors, updates dual variables, and repeats. Eventually, all robots' local model weights reach a consensus. For convex objective functions, this consensus is a global optimum. Unlike many existing methods we test our algorithm on robotics-related, deep learning tasks with nontrivial model architectures. We compare DiNNO to two benchmark distributed deep learning algorithms in (i) an MNIST image classification task, (ii) a multi-robot implicit mapping task, and (iii) a multi-robot reinforcement learning task. In these experiments we show that DiNNO performs well when faced with nonconvex deep learning objectives, time-varying communication graphs, and streaming data. In all experiments our method outperforms baselines, and was able to achieve validation loss equivalent to centrally trained models. See msl.stanford.edu/projects/dist_nn_train for videos and code. |
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| AbstractList | We present DiNNO, a distributed algorithm that enables a group of robots to collaboratively optimize a deep neural network model while communicating over a mesh network. Each robot only has access to its own data and maintains its own version of the neural network, but eventually learns a model that is as good as if it had been trained on all the data centrally. No robot sends raw data over the wireless network, preserving data privacy and ensuring efficient use of wireless bandwidth. At each iteration, each robot approximately optimizes an augmented Lagrangian function, then communicates the resulting weights to its neighbors, updates dual variables, and repeats. Eventually, all robots’ local model weights reach a consensus. For convex objective functions, this consensus is a global optimum. Unlike many existing methods we test our algorithm on robotics-related, deep learning tasks with nontrivial model architectures. We compare DiNNO to two benchmark distributed deep learning algorithms in (i) an MNIST image classification task, (ii) a multi-robot implicit mapping task, and (iii) a multi-robot reinforcement learning task. In these experiments we show that DiNNO performs well when faced with nonconvex deep learning objectives, time-varying communication graphs, and streaming data. In all experiments our method outperforms baselines, and was able to achieve validation loss equivalent to centrally trained models. See msl.stanford.edu/projects/dist_nn_train for videos and code. |
| Author | Yu, Javier Vincent, Joseph A. Schwager, Mac |
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| References | ref35 ref34 ref15 ref37 Paszke (ref2) 2019 ref14 LeCun (ref36) 1998 ref31 ref30 ref11 ref33 ref10 Koloskova (ref24) 2020 ref32 Zhang (ref13) 2018 Corder (ref7) 2019 ref1 Terry (ref42) 2020 Schulman (ref45) 2017 ref17 ref39 ref16 ref38 ref19 Tancik (ref40) 2020 ref18 Luo (ref12) 2019 ref46 Halsted (ref20) 2021 ref26 ref25 ref22 ref21 ref28 ref27 Lian (ref23) 2017 ref29 ref8 Lowe (ref41) 2017 ref9 ref4 Kingma (ref3) 2014 ref6 ref5 Papoudakis (ref43) 2021 Terry (ref44) 2020 |
| References_xml | – ident: ref39 doi: 10.1007/978-3-030-20205-7_3 – ident: ref17 doi: 10.1137/14096668X – ident: ref46 doi: 10.1007/978-3-319-71682-4_5 – start-page: 8024 volume-title: Proc. 33rd Int. Conf. Neural Inf. Process. Syst. year: 2019 ident: ref2 article-title: PyTorch: An imperative style, high-performance deep learning library – year: 2021 ident: ref20 article-title: A survey of distributed optimization methods for multi-robot systems – start-page: 5336 volume-title: Proc. 31st Int. Conf. Neural Inf. Process. Syst. year: 2017 ident: ref23 article-title: Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent – ident: ref16 doi: 10.1109/TAC.2008.2009515 – year: 2019 ident: ref7 article-title: Decentralized multi-agent actor-critic with generative inference – ident: ref37 doi: 10.1007/978-3-030-58452-8_24 – ident: ref28 doi: 10.1145/3394486.3403109 – ident: ref35 doi: 10.23919/ACC.2017.7962962 – ident: ref15 doi: 10.12681/eadd/3778 – ident: ref27 doi: 10.1109/DSW.2019.8755807 – ident: ref38 doi: 10.1109/ICCV48922.2021.00617 – year: 2020 ident: ref42 article-title: Revisiting parameter sharing in multi-agent deep reinforcement learning – year: 2020 ident: ref44 article-title: PettingZoo: Gym for multi-agent reinforcement learning – volume-title: Proc. Int. Conf. Neural Inf. Process. Syst. year: 2020 ident: ref40 article-title: Fourier features let networks learn high frequency functions in low dimensional domains – ident: ref30 doi: 10.1109/MSP.2020.2970170 – ident: ref9 doi: 10.1007/978-3-030-05816-6_3 – ident: ref1 doi: 10.1561/9781601984616 – ident: ref8 doi: 10.1109/IROS.2017.8202141 – volume-title: Proc. 35th Conf. Neural Inf. Process. Syst. Datasets Benchmarks Track year: 2021 ident: ref43 article-title: Benchmarking multi-agent deep reinforcement learning algorithms in cooperative tasks – ident: ref21 doi: 10.1109/JPROC.2018.2817461 – ident: ref10 doi: 10.1109/ICRA.2018.8460473 – year: 1998 ident: ref36 article-title: The mnist database of handwritten digits – ident: ref14 doi: 10.1109/ICRA48506.2021.9560791 – ident: ref29 doi: 10.1109/TSP.2015.2436358 – ident: ref25 doi: 10.1109/MLSP.2016.7738894 – ident: ref34 doi: 10.1109/TSP.2014.2304432 – ident: ref33 doi: 10.1007/978-3-319-46128-1_50 – ident: ref11 doi: 10.1109/LRA.2020.3048652 – ident: ref19 doi: 10.1109/TAC.2016.2525928 – ident: ref31 doi: 10.1109/TSP.2010.2055862 – ident: ref6 doi: 10.1109/ICRA.2017.7989037 – ident: ref5 doi: 10.1109/TRO.2021.3098436 – volume-title: Proc. Int. Conf. Learn. Representations year: 2020 ident: ref24 article-title: Decentralized deep learning with arbitrary communication compression – start-page: 1488 volume-title: Proc. 18th Int. Conf. Auton. Agents MultiAgent Syst. year: 2019 ident: ref12 article-title: Distributed environmental modeling and adaptive sampling for multi-robot sensor coverage – start-page: 6382 volume-title: Proc. 31st Int. Conf. Neural Inf. Process. Syst. year: 2017 ident: ref41 article-title: Multi-agent actor-critic for mixed cooperative-competitive environments – ident: ref32 doi: 10.1109/TSP.2014.2367458 – ident: ref26 doi: 10.1007/s10107-020-01487-0 – year: 2017 ident: ref45 article-title: Proximal policy optimization algorithms – year: 2014 ident: ref3 article-title: Adam: A method for stochastic optimization – start-page: 5872 volume-title: Proc. 35th Int. Conf. Mach. Learn. year: 2018 ident: ref13 article-title: Fully decentralized multi-agent reinforcement learning with networked agents – ident: ref4 doi: 10.1007/978-94-6265-282-8 – ident: ref18 doi: 10.1109/TAC.2014.2364096 – ident: ref22 doi: 10.1016/j.arcontrol.2019.05.006 |
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| SubjectTerms | Algorithms Artificial neural networks Cognitive tasks Communication Data models Deep learning Deep learning methods distributed robot systems Finite element method Image classification Iterative methods Lagrangian function Machine learning multi-robot systems Multiple robots Network management systems Neural networks Optimization Robotics Robots Task analysis Training Wireless networks |
| Title | DiNNO: Distributed Neural Network Optimization for Multi-Robot Collaborative Learning |
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