Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challenges

TL;DR

This paper explores how federated learning can enhance large-scale cloud robotic manipulation by leveraging distributed training, discussing its opportunities, challenges, and potential for scalable, privacy-preserving robotic applications.

Contribution

It connects federated learning principles with cloud robotic manipulation, highlighting opportunities and challenges for scalable, efficient, and reliable robotic systems.

Findings

Federated learning can improve cloud robotic manipulation scalability.

Challenges include ensuring reliability and efficiency in distributed settings.

Opportunities involve privacy preservation and resource optimization.

Abstract

Federated Learning (FL) is an emerging distributed machine learning paradigm, where the collaborative training of a model involves dynamic participation of devices to achieve broad objectives. In contrast, classical machine learning (ML) typically requires data to be located on-premises for training, whereas FL leverages numerous user devices to train a shared global model without the need to share private data. Current robotic manipulation tasks are constrained by the individual capabilities and speed of robots due to limited low-latency computing resources. Consequently, the concept of cloud robotics has emerged, allowing robotic applications to harness the flexibility and reliability of computing resources, effectively alleviating their computational demands across the cloud-edge continuum. Undoubtedly, within this distributed computing context, as exemplified in cloud robotic manipulation scenarios, FL offers manifold advantages while also presenting several challenges and opportunities. In this paper, we present fundamental concepts of FL and their connection to cloud robotic manipulation. Additionally, we envision the opportunities and challenges associated with realizing efficient and reliable cloud robotic manipulation at scale through FL, where researchers adopt to design and verify FL models in either centralized or decentralized settings.