QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data, making it suitable for privacy-sensitive robotic sensing applications. However, multi-agent systems generate heterogeneous and non-independent and identically distributed (non-IID) multimodal sensor streams that degrade conventional FL algorithms, while classical fusion modules introduce substantial parameter overhead and communication cost. This paper proposes QFedAgent, a hybrid quantum-classical personalized FL framework for multi-agent activity recognition. The approach integra
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- PossiblePossibly related (embedding) · 54%FareedKhan-dev/agentic-quantum-computing →
- PossiblePossibly related (embedding) · 48%Alibaba's model never trained as an agent — and improved agent performance across seven benchmarks →
- PossiblePossibly related (embedding) · 47%qualcomm/ai-hub-models →
- PossiblePossibly related (embedding) · 46%jaimasih05-commits/swarm-foraging-qlearn →
- PossiblePossibly related (embedding) · 45%WenyuChiou/awesome-agentic-ai-zh →
- LinkedLinked via arxiv author · 85%Quoc Bao Phan →
“QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition”
- LinkedLinked via arxiv author · 85%Tuy Tan Nguyen →
“QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition”
- PossiblePossibly related (embedding) · 58%tensorflow/quantum →