Federated Learning in Robotic and Autonomous Systems

Yu, Xianjia; Queralta, Jorge Peña; Heikkonen, Jukka; Westerlund, Tomi

doi:10.1016/j.procs.2021.07.041

Cited by 32 publications

(11 citation statements)

References 20 publications

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“…Other technologies, such as differential privacy, homomorphic encryption, and distributed ledger technologies (DLTs) have been used in the literature to improve FL from a systematic standpoint, making the collaborative learning process in a multi-robot system safer and privacy-preserving. FL offers potential in a variety of autonomy challenges and robotic subsystems, including cooperative SLAM, humanrobot collaborative learning, and navigation, to name a few [4].…”

Section: A Federated Learning In Roboticsmentioning

confidence: 99%

“…Connected robots open a wide variety of opportunities within the Internet of Robotic Things (IoRT) [2], specifically as mobile sensor networks capable of intelligent behavior and multi-modal data acquisition. We are particularly interested in exploring collaborative robot learning within the IoRT context [3], [4], and studying the benefits that federated learning FL can bring to distributed robotic systems.…”

Section: Introductionmentioning

confidence: 99%

“…Partly owing to the benefits of sharing knowledge without transferring raw data, FL as a privacy-preserving distributed learning has been utilized in multiple domains in robotics and autonomous system [4]. These domains include, but certainly are not limited to, navigation, cooperative SLAM based on visual-Lidar, trajectory forecasting, human-robot collaborative learning, and robot perception.…”

Section: Introductionmentioning

confidence: 99%

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Federated Learning for Vision-based Obstacle Avoidance in the Internet of Robotic Things

Yu¹,

Queralta²,

Westerlund³

2022

Preprint

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Deep learning methods have revolutionized mobile robotics, from advanced perception models for an enhanced situational awareness to novel control approaches through reinforcement learning. This paper explores the potential of federated learning for distributed systems of mobile robots enabling collaboration on the Internet of Robotic Things. To demonstrate the effectiveness of such an approach, we deploy wheeled robots in different indoor environments. We analyze the performance of a federated learning approach and compare it to a traditional centralized training process with a priori aggregated data. We show the benefits of collaborative learning across heterogeneous environments and the potential for simto-real knowledge transfer. Our results demonstrate significant performance benefits of FL and sim-to-real transfer for visionbased navigation, in addition to the inherent privacy-preserving nature of FL by keeping computation at the edge. This is, to the best of our knowledge, the first work to leverage FL for visionbased navigation that also tests results in real-world settings.

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Section: A Federated Learning In Roboticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Federated Learning for Vision-based Obstacle Avoidance in the Internet of Robotic Things

Yu¹,

Queralta²,

Westerlund³

2022

Preprint

Self Cite

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“…Federated learning enables sharing knowledge without transferring raw data. Therefore, FL allows for privacypreserving distributed learning, which can then be enhanced by other technologies, such as distributed ledger technologies [8], has been utilized in multiple domains in robotics and autonomous system [9]. To the best of our knowledge, this is the first work to introduce an strategy for continuous learning together with federated learning and validating it in the real world.…”

Section: Introductionmentioning

confidence: 99%

Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfer

Yu¹,

Queralta²,

Westerlund³

2022

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The role of deep learning (DL) in robotics has significantly deepened over the last decade. Intelligent robotic systems today are highly connected systems that rely on DL for a variety of perception, control and other tasks. At the same time, autonomous robots are being increasingly deployed as part of fleets, with collaboration among robots becoming a more relevant factor. From the perspective of collaborative learning, federated learning (FL) enables continuous training of models in a distributed, privacy-preserving way. This paper focuses on vision-based obstacle avoidance for mobile robot navigation. On this basis, we explore the potential of FL for distributed systems of mobile robots enabling continuous learning via the engagement of robots in both simulated and real-world scenarios. We extend previous works by studying the performance of different image classifiers for FL, compared to centralized, cloud-based learning with a priori aggregated data. We also introduce an approach to continuous learning from mobile robots with extended sensor suites able to provide automatically labelled data while they are completing other tasks. We show that higher accuracies can be achieved by training the models in both simulation and reality, enabling continuous updates to deployed models.

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“…In order to overcome these hurdles, decentralized versions of FL have been proposed [6]- [10]. FL has also made its niches in multi-robot scenarios [11], [12]. In such cases, each robot shares its learned model with others, thereby aiding in faster learning convergence.…”

Section: Introductionmentioning

confidence: 99%

On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios

Nair¹,

Kulkarni²,

Joshi³

et al. 2022

Preprint

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Federated Learning (FL) allows for collaboratively aggregating learned information across several computing devices and sharing the same amongst them, thereby tackling issues of privacy and the need of huge bandwidth. FL techniques generally use a central server or cloud for aggregating the models received from the devices. Such centralized FL techniques suffer from inherent problems such as failure of the central node and bottlenecks in channel bandwidth. When FL is used in conjunction with connected robots serving as devices, a failure of the central controlling entity can lead to a chaotic situation. This paper describes a mobile agent based paradigm to decentralize FL in multi-robot scenarios. Using Webots, a popular free open-source robot simulator, and Tartarus, a mobile agent platform, we present a methodology to decentralize federated learning in a set of connected robots. With Webots running on different connected computing systems, we show how mobile agents can perform the task of Decentralized Federated Reinforcement Learning (dFRL). Results obtained from experiments carried out using Q-learning and SARSA by aggregating their corresponding Q-tables, show the viability of using decentralized FL in the domain of robotics. Since the proposed work can be used in conjunction with other learning algorithms and also real robots, it can act as a vital tool for the study of decentralized FL using heterogeneous learning algorithms concurrently in multi-robot scenarios.

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Federated Learning in Robotic and Autonomous Systems

Cited by 32 publications

References 20 publications

Federated Learning for Vision-based Obstacle Avoidance in the Internet of Robotic Things

Federated Learning for Vision-based Obstacle Avoidance in the Internet of Robotic Things

Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfer

On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios

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