Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments

• URL: http://arxiv.org/abs/2507.05861v1
• Date: Tue, 08 Jul 2025 10:40:49 GMT
• Title: Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments
• Authors: Woonsang Kang, Joohyung Lee, Seungjun Kim, Jungchan Cho, Yoonseon Oh,
• Abstract summary: We propose a module-wise Federated Learning (FL) framework for Grasp pose detection.<n>FL offers a privacy-preserving solution, but its application to GPD is hindered by the substantial communication overhead of large models.<n>Our work presents a communication-efficient framework for training robust, generalized GPD models in a decentralized manner.
• Score: 10.63791848873399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Grasp pose detection (GPD) is a fundamental capability for robotic autonomy, but its reliance on large, diverse datasets creates significant data privacy and centralization challenges. Federated Learning (FL) offers a privacy-preserving solution, but its application to GPD is hindered by the substantial communication overhead of large models, a key issue for resource-constrained robots. To address this, we propose a novel module-wise FL framework that begins by analyzing the learning dynamics of the GPD model's functional components. This analysis identifies slower-converging modules, to which our framework then allocates additional communication effort. This is realized through a two-phase process: a standard full-model training phase is followed by a communication-efficient phase where only the identified subset of slower-converging modules is trained and their partial updates are aggregated. Extensive experiments on the GraspNet-1B dataset demonstrate that our method outperforms standard FedAvg and other baselines, achieving higher accuracy for a given communication budget. Furthermore, real-world experiments on a physical robot validate our approach, showing a superior grasp success rate compared to baseline methods in cluttered scenes. Our work presents a communication-efficient framework for training robust, generalized GPD models in a decentralized manner, effectively improving the trade-off between communication cost and model performance.

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