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

• URL: http://arxiv.org/abs/2507.17903v1
• Date: Wed, 23 Jul 2025 20:01:36 GMT
• Title: Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challenges
• Authors: Obaidullah Zaland, Chanh Nguyen, Florian T. Pokorny, Monowar Bhuyan,
• Abstract summary: Federated Learning (FL) is an emerging distributed machine learning paradigm.<n>Within this distributed computing context, FL offers manifold advantages while also presenting several challenges and opportunities.<n>We envision the opportunities and challenges associated with realizing efficient and reliable cloud robotic manipulation at scale through FL.
• Score: 7.338353383261602
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• TIFeD: a Tiny Integer-based Federated learning algorithm with Direct feedback alignment [47.39949471062935]
  Training machine and deep learning models directly on resource-constrained devices is the next challenge in the field of tiny machine learning. The proposedeD algorithm, with its full-network and single-layer implementations, is made available to the scientific community as a public repository.
  arXiv  Detail & Related papers  (2024-11-25T14:44:26Z)
• Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation [7.8839937556789375]
  Federated-EmbedCluster (Fed-EC) is a clustering-based federated learning framework deployed with vision based autonomous robot navigation in diverse outdoor environments. Fed-EC reduces the communication size by 23x for each robot while matching the performance of centralized learning for goal-oriented navigation and outperforms local learning.
  arXiv  Detail & Related papers  (2024-11-06T18:44:09Z)
• OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning [29.798780069556074]
  Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices. We propose OnDev-LCT: Lightweight Convolutional Transformers for On-Device vision tasks with limited training data and resources.
  arXiv  Detail & Related papers  (2024-01-22T02:17:36Z)
• Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights [52.024964564408]
  This paper examines the added-value of implementing Federated Learning throughout all levels of the protocol stack. It presents important FL applications, addresses hot topics, provides valuable insights and explicits guidance for future research and developments. Our concluding remarks aim to leverage the synergy between FL and future 6G, while highlighting FL's potential to revolutionize wireless industry.
  arXiv  Detail & Related papers  (2023-12-07T20:39:57Z)
• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications [6.042202852003457]
  Federated learning (FL) is a technique for developing robust machine learning (ML) models. To protect user privacy, FL requires users to send model updates rather than transmitting large quantities of raw and potentially confidential data. This survey provides a comprehensive analysis and comparison of the most recent FL algorithms.
  arXiv  Detail & Related papers  (2023-10-08T19:54:26Z)
• Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly [62.473245910234304]
  This paper takes a hardware-centric approach to explore how Large Language Models can be brought to modern edge computing systems. We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions.
  arXiv  Detail & Related papers  (2023-10-04T20:27:20Z)
• Federated Learning and Meta Learning: Approaches, Applications, and Directions [94.68423258028285]
  In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta) Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
  arXiv  Detail & Related papers  (2022-10-24T10:59:29Z)
• On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios [0.0]
  Federated Learning (FL) allows for collaboratively aggregating learned information across several computing devices. 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.
  arXiv  Detail & Related papers  (2022-07-19T16:17:12Z)
• Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems [44.627847349764664]
  Next-generation industrial systems have driven advances in ultra-reliable, low latency communications. Distributed machine learning (FL) represents a mushrooming multidisciplinary research area weaving in sensing, communication and learning. This article explores emerging opportunities of FL for the next-generation networked industrial systems.
  arXiv  Detail & Related papers  (2021-01-09T14:27:52Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.