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

2025-07-23

AI Summary

To address the challenges of limited local computational resources, high communication latency, and inadequate data privacy preservation in large-scale robotic collaboration within cloud environments, this paper proposes a federated learning framework tailored for cloud robotic operations. The framework integrates cloud-edge collaborative computing with distributed model aggregation, supporting both centralized and decentralized training architectures to enable dynamic robot enrollment, low-overhead model updates, and privacy-preserving cross-robot collaborative learning. Its key innovation lies in deeply embedding federated learning into the robotic operational control loop—thereby jointly optimizing real-time responsiveness, scalability, and security. Experimental evaluation demonstrates that the proposed method significantly improves task collaboration efficiency (+23.6%) and enhances model convergence stability, all while ensuring raw data remain on-device. This work establishes a deployable, privacy-aware learning paradigm for large-scale intelligent robotic swarms.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Federated Learning enables collaborative model training without sharing private data
• Cloud robotics addresses limited computing resources in robotic manipulation tasks
• FL in cloud robotics presents both opportunities and challenges for scalability

Innovation

• Federated Learning enables distributed model training
• Cloud robotics alleviates computational resource constraints
• FL models designed for centralized or decentralized settings