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FedRobo: Federated Learning Driven Autonomous Inter Robots Communication For Optimal Chemical Sprays

arXiv 2408.06382 ↗First posted 10 Aug 2024cs.LG cs.CV cs.DCcited by 0

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Abstract (arXiv)

Federated Learning enables robots to learn from each other's experiences without relying on centralized data collection. Each robot independently maintains a model of crop conditions and chemical spray effectiveness, which is periodically shared with other robots in the fleet. A communication protocol is designed to optimize chemical spray applications by facilitating the exchange of information about crop conditions, weather, and other critical factors. The federated learning algorithm leverages this shared data to continuously refine the chemical spray strategy, reducing waste and improving crop yields. This approach has the potential to revolutionize the agriculture industry by offering a scalable and efficient solution for crop protection. However, significant challenges remain, including the development of a secure and robust communication protocol, the design of a federated learning algorithm that effectively integrates data from multiple sources, and ensuring the safety and reliability of autonomous robots. The proposed cluster-based federated learning approach also effectively reduces the computational load on the global server and minimizes communication overhead among clients.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 35 references that are in the map. First posted on arXiv 10 Aug 2024.

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References · 11

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2019Towards Federated Learning at Scale: System Designcited by 832
  5. 2020The Future of Digital Health with Federated Learningcited by 633
  6. 2021Federated Learning: Opportunities and Challengescited by 84
  7. 2022Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfercited by 6
  8. 2023Accurate Crop Spraying with RTK and Machine Learning on an Autonomous Field Robotcited by 2
  9. 2024SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environmentcited by 1
  10. 2024FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAGcited by 1
  11. 2024SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networkscited by 1

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