arXiv:2602.14401v2 Announce Type: replace-cross Abstract: Vision-Language Navigation VLN requires large-scale trajectory instruction data from private indoor environments, raising significant privacy concerns. Federated Learning FL mitigates this by keeping data on-device, but vanilla FL struggles under VLNs' extreme cross-client heterogeneity in environments and instruction styles, making a single global model suboptimal. This paper proposes pFedNavi, a structure-aware and dynamically adaptive personalized federated learning framework tailored for VLN. Our key idea is to personalize where it
The increasing sophistication of embodied AI and the growing concerns over data privacy are driving the need for more robust and secure training methodologies.
This development addresses critical privacy concerns in embodied AI training while enhancing the performance of federated learning in complex, heterogeneous environments, paving the way for wider adoption.
The ability to train embodied AI models effectively on decentralized, private data sets without sacrificing performance opens new avenues for development and deployment in sensitive applications.
- · Embodied AI developers
- · Privacy-focused AI companies
- · Smart home technology providers
- · Robotics companies
- · Centralized data platforms for AI training
- · Companies relying solely on massive centralized datasets
Embodied AI systems will become more adaptable to individual user environments and preferences without compromising data privacy.
Increased trust in embodied AI systems due to enhanced privacy could accelerate their integration into sensitive personal and professional environments.
This could enable a new wave of personalized and decentralized AI applications, reducing the dependency on large-scale data collection by single entities.
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Read at arXiv cs.AI