Decentralized Sharing and Valuation of Fleet Robotic Data

Yuchong Geng, Dongyue Zhang, Po-han Li, Oguzhan Akcin, Ao Tang, Sandeep P. Chinchali
Proceedings of the 5th Conference on Robot Learning, PMLR 164:1795-1800, 2022.

Abstract

We propose a decentralized learning framework for robots to trade, price, and discover valuable machine learning (ML) training data. Today’s robotic fleets, such as self-driving vehicles, can gather terabytes of rich video and LIDAR data in diverse, geo-distributed environments. Often, robots in one city or home might observe training data that is commonplace for them, but is actually a valuable, out-of-distribution (OoD) dataset to train robust ML models at robots elsewhere. However, simply sharing all this diverse data in cloud databases is infeasible due to limits on privacy and network bandwidth. Inspired by decentralized file sharing protocols like BitTorrent, we propose a novel system where each robot is provisioned with a learnable privacy filter and sharing model. Importantly, this sharing model attempts to predict and prioritize which sensory percepts are of high value to other robotic peers using a decentralized voting and feedback mechanism. Our scheme naturally raises timely questions on data privacy and valuation as companies start to deploy robots in our homes, hospitals, and roads.

Cite this Paper

BibTeX
@InProceedings{pmlr-v164-geng22a, title = {Decentralized Sharing and Valuation of Fleet Robotic Data}, author = {Geng, Yuchong and Zhang, Dongyue and Li, Po-han and Akcin, Oguzhan and Tang, Ao and Chinchali, Sandeep P.}, booktitle = {Proceedings of the 5th Conference on Robot Learning}, pages = {1795--1800}, year = {2022}, editor = {Faust, Aleksandra and Hsu, David and Neumann, Gerhard}, volume = {164}, series = {Proceedings of Machine Learning Research}, month = {08--11 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v164/geng22a/geng22a.pdf}, url = {https://proceedings.mlr.press/v164/geng22a.html}, abstract = {We propose a decentralized learning framework for robots to trade, price, and discover valuable machine learning (ML) training data. Today’s robotic fleets, such as self-driving vehicles, can gather terabytes of rich video and LIDAR data in diverse, geo-distributed environments. Often, robots in one city or home might observe training data that is commonplace for them, but is actually a valuable, out-of-distribution (OoD) dataset to train robust ML models at robots elsewhere. However, simply sharing all this diverse data in cloud databases is infeasible due to limits on privacy and network bandwidth. Inspired by decentralized file sharing protocols like BitTorrent, we propose a novel system where each robot is provisioned with a learnable privacy filter and sharing model. Importantly, this sharing model attempts to predict and prioritize which sensory percepts are of high value to other robotic peers using a decentralized voting and feedback mechanism. Our scheme naturally raises timely questions on data privacy and valuation as companies start to deploy robots in our homes, hospitals, and roads. } }
Endnote
%0 Conference Paper %T Decentralized Sharing and Valuation of Fleet Robotic Data %A Yuchong Geng %A Dongyue Zhang %A Po-han Li %A Oguzhan Akcin %A Ao Tang %A Sandeep P. Chinchali %B Proceedings of the 5th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2022 %E Aleksandra Faust %E David Hsu %E Gerhard Neumann %F pmlr-v164-geng22a %I PMLR %P 1795--1800 %U https://proceedings.mlr.press/v164/geng22a.html %V 164 %X We propose a decentralized learning framework for robots to trade, price, and discover valuable machine learning (ML) training data. Today’s robotic fleets, such as self-driving vehicles, can gather terabytes of rich video and LIDAR data in diverse, geo-distributed environments. Often, robots in one city or home might observe training data that is commonplace for them, but is actually a valuable, out-of-distribution (OoD) dataset to train robust ML models at robots elsewhere. However, simply sharing all this diverse data in cloud databases is infeasible due to limits on privacy and network bandwidth. Inspired by decentralized file sharing protocols like BitTorrent, we propose a novel system where each robot is provisioned with a learnable privacy filter and sharing model. Importantly, this sharing model attempts to predict and prioritize which sensory percepts are of high value to other robotic peers using a decentralized voting and feedback mechanism. Our scheme naturally raises timely questions on data privacy and valuation as companies start to deploy robots in our homes, hospitals, and roads.
APA
Geng, Y., Zhang, D., Li, P., Akcin, O., Tang, A. & Chinchali, S.P.. (2022). Decentralized Sharing and Valuation of Fleet Robotic Data. Proceedings of the 5th Conference on Robot Learning, in Proceedings of Machine Learning Research 164:1795-1800 Available from https://proceedings.mlr.press/v164/geng22a.html.

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